.gitignore

+bias-venv/
+.vscode/
+.ipynb_checkpoints/
+cc.de.300.bin.gz
+GloVe-master/
\ No newline at end of file

README.md

+# Analysing and Mitigating Origin Bias in German Word Embeddings
+
+- Bachelor's thesis
+- Author: Aileen Reichelt
+- Supervision: Prof. Dr. Katja Markert
+- Heidelberg University, Institute of Computational Linguistics
+- Submitted January 23, 2024

WEAT/run_weat.sh

+#!/bin/bash
+#
+#SBATCH --job-name=weat
+#SBATCH --output=weat_output_4.txt
+#SBATCH --mem=32G
+#SBATCH --partition=compute
+#SBATCH --cpus-per-task=32
+#SBATCH --mail-user=reichelt@cl.uni-heidelberg.de
+#SBATCH --mail-type=ALL
+#SBATCH --time=3-00:00:00
+
+# JOB STEPS
+source /home/students/reichelt/ba/bias-mitigation-ba/bias-venv/bin/activate
+
+srun python /home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py --attribute italian --vector_location /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_polish_w2vformat.txt
+srun python /home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py --attribute turkish --vector_location /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_italian_w2vformat.txt
+srun python /home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py --attribute polish --vector_location /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_italian_w2vformat.txt

WEAT/weat_experiments.py

+"""
+A .py version of the WEAT experiments notebook, created so I can
+run the script using SBATCH with various parameters. Limited to
+German options and does not include W2V, unlike notebook.
+
+Parallelizes the permutation test.
+
+Checks bias in various pre-trained embeddings using the WEAT. Different
+embeddings and attribute lists can be chosen.
+"""
+import argparse
+from itertools import combinations, islice
+from sklearn.metrics.pairwise import cosine_similarity as cosine
+import numpy as np
+from gensim.models import KeyedVectors
+from concurrent.futures import ProcessPoolExecutor
+
+
+def get_target_words(version: str) -> dict:
+    """
+    load German target words for origin as per Kurpicz-Briki 2020 (WEAT 5),
+    or alternatively WEAT 6 target words from Caliskan et al 2017
+    """
+    if version == "reproduction":
+        pleasant = {  # aka career
+            "executive": [], "management": [], "professional": [], "corporation": [],
+            "salary": [], "office": [], "business": [], "career": [],
+        }
+        unpleasant = {  # aka family
+            "home": [], "parents": [], "children": [], "family": [], "cousins": [],
+            "marriage": [], "wedding": [], "relatives": [],
+        }
+    else:
+        pleasant = {
+            "spaß": [],
+            "liebe": [],
+            "frieden": [],
+            "wunderbar": [],
+            "freude": [],
+            "lachen": [],
+            "glück": [],
+        }
+        unpleasant = {
+            "qual": [],
+            "furchtbar": [],
+            "schrecklich": [],
+            "übel": [],
+            "böse": [],
+            "krieg": [],
+            "grausam": [],
+            "versagen": []
+        }
+    return {"pleasant": pleasant, "unpleasant": unpleasant}
+
+
+def get_attribute_words(version: str) -> dict:
+    """Load name lists depending on country. Alternatively load version by Kurpicz-Briki"""
+    if version == "kurpicz":
+        german = {
+            "Peter": [], "Daniel": [], "Hans": [], "Thomas": [], "Andreas": [],
+            "Martin": [], "Markus": [], "Michael": [], "Maria": [], "Anna": [],
+            "Ursula": [], "Ruth": [], "Monika": [], "Elisabeth": [], "Verena": [],
+            "Sandra": []
+        }
+
+        foreign = {
+            "Ladina": [], "Fatima": [], "Fatma": [], "Alma": [], "Soraya": [],
+            "Svetlana": [], "Elif": [], "Vesna": [], "Mehmet": [], "Mustafa": [],
+            "Aleksandar": [], "Mohamed": [], "Ibrahim": [], "Dragan": [],
+            "Hasan": [], "Mohammad": []
+        }
+
+        return {"german": german, "foreign": foreign}
+    elif version == "reproduction":
+        german = {  # aka male
+            "John": [], "Paul": [], "Mike": [], "Kevin": [], "Steve": [],
+            "Greg": [], "Jeff": [], "Bill": []
+        }
+        foreign = {  # aka female
+            "Amy": [], "Joan": [], "Lisa": [], "Sarah": [], "Diana": [],
+            "Kate": [], "Ann": [], "Donna": []
+        }
+        return {"german": german, "foreign": foreign}
+    else:
+        german = {
+            "Katharina": [], "Susanne": [], "Karin": [], "Ulrike": [], "Renate": [],
+            "Birgit": [], "Bettina": [], "Jutta": [], "Ute": [], "Cornelia": [],
+            "Katja": [], "Heike": [], "Stefanie": [], "Kerstin": [], "Tanja": [],
+            "Hans": [], "Carl": [], "Wolfgang": [], "Andreas": [], "Werner": [],
+            "Christoph": [], "Klaus": [], "Philipp": [], "Joachim": [], "Jürgen": [],
+            "Dieter": [], "Matthias": [], "Manfred": [], "Sebastian": [], "Rainer": []
+        }
+
+        if version == "turkish":
+            foreign = {
+                "Esra": [], "Merve": [], "Fatma": [], "Sibel": [], "Elif": [], "Ayşe": [],
+                "Emine": [], "Özlem": [], "Zeynep": [], "Hatice": [], "Dilek": [], "Ebru": [],
+                "Pınar": [], "Hülya": [], "Derya": [], "Mustafa": [], "Murat": [],
+                "Ahmet": [], "Kemal": [], "Orhan": [], "Hüseyin": [], "Bülent": [],
+                "Metin": [], "Ömer": [], "Emre": [], "Halil": [], "Erkan": [],
+                "Uğur": [], "Burak": [], "Volkan": []
+            }
+
+        elif version == "polish":
+            foreign = {
+                "Magdalena": [], "Ewa": [], "Zofia": [], "Beata": [], "Katarzyna": [],
+                "Krystyna": [], "Małgorzata": [], "Jadwiga": [], "Danuta": [],
+                "Elżbieta": [], "Urszula": [], "Alicja": [], "Aneta": [], "Iwona": [],
+                "Edyta": [], "Andrzej": [], "Stanisław": [], "Marek": [], "Józef": [],
+                "Henryk": [], "Krzysztof": [], "Władysław": [], "Tadeusz": [], "Piotr": [],
+                "Janusz": [], "Tomasz": [], "Wojciech": [], "Jakub": [], "Marcin": [],
+                "Franciszek": []
+            }
+
+        elif version == "italian":
+            foreign = {
+                "Caterina": [], "Francesca": [], "Paola": [], "Giulia": [], "Chiara": [],
+                "Giovanna": [], "Alessandra": [], "Gioia": [], "Antonella": [],
+                "Giuseppina": [], "Azzurra": [], "Antonietta": [], "Ambra": [],
+                "Alessia": [], "Giorgia": [], "Giovanni": [], "Carlo": [],
+                "Francesco": [], "Giuseppe": [], "Pietro": [], "Luigi": [], "Paolo": [],
+                "Alessandro": [], "Angelo": [], "Giorgio": [], "Domenico": [],
+                "Enrico": [], "Stefano": [], "Vincenzo": [], "Matteo": []
+            }
+
+        else:
+            raise ValueError("Invalid version specified. See --help")
+
+        return {"german": german, "foreign": foreign}
+
+
+def get_embeddings(lookup_dict: dict, embeddings) -> dict():
+    """Go through nested seed dicts and look up embedding for each word"""
+    for category, seeds in lookup_dict.items():
+        for word, _ in seeds.items():
+            if word.lower() in embeddings:
+                seeds[word] = embeddings[word.lower()]
+            else:
+                raise KeyError(f"'{word}' not in vocabulary")
+        lookup_dict[category] = seeds
+    return lookup_dict
+
+
+def attribute_association_s(word_vector, target_set1, target_set2):
+    reshaped_word_vector = np.array(word_vector).reshape(1, -1)
+    sims1 = [cosine(reshaped_word_vector, np.array(vec).reshape(1, -1)) for vec in list(target_set1.values())]
+    sims2 = [cosine(reshaped_word_vector, np.array(vec).reshape(1, -1)) for vec in list(target_set2.values())]
+    return np.mean(sims1) - np.mean(sims2)
+
+
+def differential_association_s(attr1, attr2, target1, target2):
+    sum1 = sum([attribute_association_s(vec, target1, target2) for vec in list(attr1.values())])
+    sum2 = sum([attribute_association_s(vec, target1, target2) for vec in list(attr2.values())])
+    return sum1 - sum2
+
+
+def cohens_d_calc(target1, target2, attr1, attr2):
+    mean1 = np.mean([attribute_association_s(x, attr1, attr2) for x in list(target1.values())])
+    mean2 = np.mean([attribute_association_s(x, attr1, attr2) for x in list(target2.values())])
+    join = list(target1.values()) + (list(target2.values()))
+    joint_association = [attribute_association_s(x, attr1, attr2) for x in join]
+    stddev = np.std(joint_association)
+    return (mean1 - mean2) / stddev
+
+
+def permutations(target1, target2):
+    join = list(target1.keys()) + list(target2.keys())
+    combs = list(islice(combinations(join, int(len(join)/2)), 100000))
+    first_groups = []
+    second_groups = []
+    for c in combs:
+        rest = []
+        for e in join:
+            if e not in c:
+                rest.append(e)
+        first_groups.append(c)
+        second_groups.append(rest)
+    return first_groups, second_groups
+
+
+def p_value_calc_worker(args):
+    X_subset, Y_subset, comparison, attr1, attr2 = args
+    return differential_association_s(X_subset, Y_subset, attr1, attr2) > comparison
+
+def p_value_calc(comparison, X_perms, Y_perms, target1, target2, attr1, attr2):
+    counter = 0
+    joint_dict = {**target1, **target2}
+
+    with ProcessPoolExecutor() as executor:
+        args_list = []
+        for i, _ in enumerate(X_perms):
+            X_subset = {key: joint_dict[key] for key in X_perms[i]}
+            Y_subset = {key: joint_dict[key] for key in Y_perms[i]}
+            args_list.append((X_subset, Y_subset, comparison, attr1, attr2))
+
+        results = list(executor.map(p_value_calc_worker, args_list))
+        counter = sum(results)
+
+    return counter
+
+def calculate_WEAT(target_data: dict, attribute_data: dict) -> tuple:
+    X = attribute_data["german"]
+    Y = attribute_data["foreign"]
+    A = target_data["pleasant"]
+    B = target_data["unpleasant"]
+
+    original_diff_association = differential_association_s(X, Y, A, B)
+    d = cohens_d_calc(X, Y, A, B)
+    X_i, Y_i = permutations(X, Y)
+    p_value_count = p_value_calc(original_diff_association, X_i, Y_i, X, Y, A, B)
+    p = p_value_count/100000
+
+    return d, p
+
+
+
+if __name__ == "__main__":
+    np.random.seed(42)
+
+    parser = argparse.ArgumentParser(
+        description="Calculate WEAT score for given attributes and vectors")
+    parser.add_argument("--attribute", help="'kurpicz', 'turkish', 'polish', 'reproduction'")
+    parser.add_argument("--vector_location", help="specify a file path to embeddings")
+    args = parser.parse_args()
+
+    print("Loading seed words...")
+    target_dicts = get_target_words(args.attribute)
+    attribute_dicts = get_attribute_words(args.attribute)
+
+    print("Loading model...")
+    model = KeyedVectors.load_word2vec_format(args.vector_location, binary=False)
+
+    print("Retrieving embeddings...")
+    target_dicts = get_embeddings(target_dicts, model)
+    attribute_dicts = get_embeddings(attribute_dicts, model)
+
+    print("Calculating WEAT...")
+    cohens_d, p_value = calculate_WEAT(target_dicts, attribute_dicts)
+
+    print(f"WEAT scores for: {args.attribute} test, vectors from {args.vector_location}")
+    print(f"Cohen's d: {cohens_d:.4f}, p-value: {p_value:.4f}")
+    print("-----------------------------------------------")

WEAT/weat_output.txt

+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/wiki.de.vec
+Cohen's d: 1.3571, p-value: 0.0109
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/wiki.de.vec
+Cohen's d: 0.2829, p-value: 0.5185
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/wiki.de.vec
+Cohen's d: 1.0331, p-value: 0.1082
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_turkish_w2vformat.txt
+Cohen's d: 1.1332, p-value: 0.0573
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_polish_w2vformat.txt
+Cohen's d: 0.1786, p-value: 0.5699
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_italian_w2vformat.txt
+Cohen's d: 0.5896, p-value: 0.3510
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_turkish_w2vformat.txt
+Cohen's d: 0.2123, p-value: 0.5639
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_turkish_w2vformat.txt
+Cohen's d: 0.9480, p-value: 0.1469
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_polish_w2vformat.txt
+Cohen's d: 1.3612, p-value: 0.0103
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_polish_w2vformat.txt
+Cohen's d: 1.0477, p-value: 0.1018
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_italian_w2vformat.txt
+Cohen's d: 1.3299, p-value: 0.0139
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/fasttext/fasttext_hard_debiased_italian_w2vformat.txt
+Cohen's d: 0.1901, p-value: 0.5747
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Traceback (most recent call last):
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 212, in <module>
+    target_dicts = get_embeddings(target_dicts, model)
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 117, in get_embeddings
+    raise KeyError(f"'{word}' not in vocabulary")
+KeyError: "'scheußlich' not in vocabulary"
+srun: error: node37: task 0: Exited with exit code 1

WEAT/weat_output_2.txt

+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: kurpicz test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/vectors_no_debiasing.txt
+Cohen's d: 1.7889, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/vectors_no_debiasing.txt
+Cohen's d: 1.8321, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/vectors_no_debiasing.txt
+Cohen's d: 1.4650, p-value: 0.0004
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_turkish.txt
+Cohen's d: 1.8209, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_polish.txt
+Cohen's d: 1.5567, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_italian.txt
+Cohen's d: 1.3057, p-value: 0.0001
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_turkish.txt
+Cohen's d: 1.6582, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_turkish.txt
+Cohen's d: 1.4522, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_polish.txt
+Cohen's d: 1.7717, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_polish.txt
+Cohen's d: 1.3303, p-value: 0.0001
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_italian.txt
+Cohen's d: 1.7891, p-value: 0.0000
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_dd_italian.txt
+Cohen's d: 1.7005, p-value: 0.0000
+-----------------------------------------------
+

WEAT/weat_output_3.txt

+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_turkish_w2vformat.txt
+Cohen's d: 1.5315, p-value: 0.0003
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_turkish_w2vformat.txt
+Cohen's d: 1.1688, p-value: 0.0115
+-----------------------------------------------
+
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_polish_w2vformat.txt
+Cohen's d: 1.7833, p-value: 0.0000
+-----------------------------------------------
+
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 241
+    print("-----------------------------------------------)
+                                                          ^
+SyntaxError: EOL while scanning string literal
+srun: error: node37: task 0: Exited with exit code 1
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 241
+    print("-----------------------------------------------)
+                                                          ^
+SyntaxError: EOL while scanning string literal
+srun: error: node37: task 0: Exited with exit code 1
+  File "/home/students/reichelt/ba/bias-mitigation-ba/WEAT/weat_experiments.py", line 241
+    print("-----------------------------------------------)
+                                                          ^
+SyntaxError: EOL while scanning string literal
+srun: error: node37: task 0: Exited with exit code 1

WEAT/weat_output_4.txt

+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: italian test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_polish_w2vformat.txt
+Cohen's d: 1.2579, p-value: 0.0046
+-----------------------------------------------
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: turkish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_italian_w2vformat.txt
+Cohen's d: 1.7934, p-value: 0.0000
+-----------------------------------------------
+Loading seed words...
+Loading model...
+Retrieving embeddings...
+Calculating WEAT...
+WEAT scores for: polish test, vectors from /home/students/reichelt/ba/bias-mitigation-ba/data/embeddings/glove/dd-glove/glove_hard_debiased_italian_w2vformat.txt
+Cohen's d: 1.6358, p-value: 0.0000
+-----------------------------------------------

WordSim353/compare.py

+"""Compare similarity scores which one set of GloVe embeddings generates
+versus another set of GloVe embeddings to determine whether the two sets
+of embeddings are more or less the same."""
+
+import pandas as pd
+from gensim.models import KeyedVectors
+from scipy.stats import spearmanr
+from scipy.spatial.distance import cosine
+
+stanford_path = "/workspace/students/reichelt/ba_data/embeddings/glove/glove.6B.300d_w2vformat.txt"
+dd_glove_path = "/workspace/students/reichelt/ba_data/embeddings/glove/dd-glove/english_vectors_no_debiasing.txt"
+
+stanford_model = KeyedVectors.load_word2vec_format(stanford_path, binary=False)
+stanford_model.init_sims(replace=True)
+dd_glove_model = KeyedVectors.load_word2vec_format(dd_glove_path, binary=False)
+stanford_model.init_sims(replace=True)
+
+ws353_data = pd.read_csv("/home/students/reichelt/ba/bias-mitigation-ba/WordSim353/data/original_finkelstein/combined.csv")
+
+def calculate_embedding_similarity(row, embedding):
+    if embedding=="stanford":
+        model = stanford_model
+    elif embedding=="dd":
+        model = dd_glove_model
+    else:
+        raise ValueError
+
+    word_1 = row["Word 1"].lower()
+    word_2 = row["Word 2"].lower()
+
+    if word_1 in model:
+        embd_1 = model[word_1]
+    else:
+        print(f"{word_1} not in vocab. returning similarity 0.")
+        return 0.0
+    if word_2 in model:
+        embd_2 = model[word_2]
+    else:
+        print(f"{word_2} not in vocab. returning similarity 0.")
+        return 0.0
+
+    return 1 - cosine(embd_1, embd_2)
+
+def calculate_stanford(row):
+    word_1 = row["Word 1"].lower()
+    word_2 = row["Word 2"].lower()
+    if word_1 in stanford_model:
+        embd_1 = stanford_model[word_1]
+    else:
+        print(f"{word_1} not in vocab. returning similarity 0.")
+        return 0.0
+    if word_2 in stanford_model:
+        embd_2 = stanford_model[word_2]
+    else:
+        print(f"{word_2} not in vocab. returning similarity 0.")
+        return 0.0
+    return 1 - cosine(embd_1, embd_2)
+
+def calculate_dd_glove(row):
+    word_1 = row["Word 1"].lower()
+    word_2 = row["Word 2"].lower()
+    if word_1 in dd_glove_model:
+        embd_1 = dd_glove_model[word_1]
+    else:
+        print(f"{word_1} not in vocab. returning similarity 0.")
+        return 0.0
+    if word_2 in dd_glove_model:
+        embd_2 = dd_glove_model[word_2]
+    else:
+        print(f"{word_2} not in vocab. returning similarity 0.")
+        return 0.0
+    return 1 - cosine(embd_1, embd_2)
+
+ws353_data["Stanford Score"] = ws353_data.apply(calculate_stanford, axis=1)
+ws353_data["DD-GloVe Score"] = ws353_data.apply(calculate_dd_glove, axis=1)
+
+rho, p = spearmanr(
+        ws353_data["Stanford Score"], ws353_data["DD-GloVe Score"],
+        nan_policy="raise"
+    )
+
+print("rho: " + str(rho))
+print("p: " + str(p))

WordSim353/data/german_gurevych_zesch/gur_350.tsv

+#WORD1	WORD2	Value	POS1	POS2
+Absage	ablehnen	3.5	n	v
+Absage	Stellenanzeige	1.88	n	n
+Affe	Gepäckkontrolle	0.13	n	n
+Affe	Makake	4	n	n
+Afrika	historisch	1	n	a
+Agentur	Irrtum	0	n	n
+Airbag	Kopfairbag	3.88	n	n
+analysieren	Analyse	3.88	v	n
+Ansehen	Schaden	0.88	n	n
+Arbeitssuchender	Bewerbung	2.75	n	n
+aufklären	erklären	2.5	v	v
+Aufpreis	Grundpreis	3.13	n	n
+Aufstieg	Erfolg	3.25	n	n
+aufzeichnen	schreiben	2.75	v	v
+Aussage	Auftritt	1.38	n	n
+Aussage	Rede	2.38	n	n
+Aussage	sagen	3.38	n	v
+Aussterben	bedrohen	2.13	n	v
+Auto	fahren	3.5	n	v
+Bayern	Bayerisch	4	n	a
+Bayern	Deutschland	3.5	n	n
+Bayern	weißblau	2.75	n	a
+Beamte	Amt	3.63	n	n
+beginnen	dauern	2.38	v	v
+begründen	ausgehen	0.88	v	v
+Behörde	Vorschrift	2.75	n	n
+beinhalten	umfassen	3.25	v	v
+Benedetto	Benedikt	3.63	n	n
+Benziner	Dieselversion	3	n	n
+Berlin	Berlin-Kreuzberg	3.38	n	n
+Berufstätigkeit	Erfolg	2.13	n	n
+beschleunigen	übertreiben	1.13	v	v
+beschuldigen	Mitschuld	2.5	v	n
+Besucher	bekommen	1.38	n	v
+Bewerbung	Job	2.38	n	n
+Bild	ähneln	1.38	n	v
+Bild	Grafik	3.13	n	n
+Bild	Röntgenaufnahme	3	n	n
+Bild	Symbol	2.13	n	n
+Bild	visuell	3	n	a
+Böse	Gott	2	n	n
+Botschaft	sichtbar	0.25	n	a
+Büro	Schreibtisch	3	n	n
+Demut	demütig	4	n	a
+demütig	selbstbewusst	1.88	a	a
+Design	Optik	2.63	n	n
+Designer	Eleganz	2.63	n	n
+deutsch	Deutscher	3.88	a	n
+Deutscher	Bundesbürger	3.5	n	n
+Deutschland	Europa	3.25	n	n
+Ding	Gegenstand	4	n	n
+Doktorandin	Abteilung	1.88	n	n
+Doktorandin	Dissertationsthema	2.63	n	n
+Drehmoment	drehfreudig	1.75	n	a
+dringend	rasch	2.38	a	a
+Durchsicht	sehen	2.75	n	v
+einfach	komplex	2.75	a	a
+Einkommen	Gehaltsunterschied	2	n	n
+Einrichtung	Interior	3.5	n	n
+Einsamkeit	allein	3.5	n	a
+einsteigen	aussteigen	2.75	v	v
+Eleganz	klobig	1.38	n	a
+Eltern	Vater	3.5	n	n
+entgehen	bewundern	0.13	v	v
+entwickeln	Entwicklungschef	2.63	v	n
+Erfolg	erfolgreich	4	n	a
+Erfolg	Maßstab	1.25	n	n
+erforschen	herausfinden	3.13	v	v
+Erhalt	bedroht	1	n	a
+erkennen	sehen	3	v	v
+erklären	begründen	2.5	v	v
+erklären	machen	0.5	v	v
+ernst	ironisch	2	a	a
+erst	Ursprungsort	1.38	a	n
+Erwachsener	Geist	0	n	n
+Erwachsener	Kinder	2.63	n	n
+erwarten	klären	0	v	v
+fahren	Automobil	3	v	n
+filtern	herausfiltern	3.63	v	v
+filtern	selektieren	3.38	v	v
+finden	herausfinden	3	v	v
+Fisch	schwimmen	3.38	n	v
+Flaschenöffner	Küchenwerkzeug	3.63	n	n
+fokussieren	Aufmerksamkeit	2.63	v	n
+folgen	sortieren	0.25	v	v
+Form	Farbe	2.13	n	n
+formulieren	Formulierung	3.88	v	n
+Formulierung	Stiftung	0.13	n	n
+Forscher	Wissenschaftler	3.88	n	n
+Frage	Antwort	3.25	n	n
+Franzose	Deutscher	2.38	n	n
+Frau	Familie	2.75	n	n
+Frau	Mann	3.25	n	n
+Frühlingssonne	kitzeln	1.25	n	v
+Frust	frustrieren	3.88	n	v
+Frust	Leidensgenosse	1.88	n	n
+Frust	Rache	1.88	n	n
+geben	nehmen	3.25	v	v
+Gefühl	Frau	1.75	n	n
+Gegenwind	kritisieren	0.5	n	v
+Gehege	Zoo	2.63	n	n
+Gehirn	Kortex	3.25	n	n
+Gehirn	verstehen	2.13	n	v
+gemeinsam	leben	1	a	v
+Generation	Jugendlicher	2.5	n	n
+geografisch	praktisch	0.13	a	a
+Gepäckkontrolle	Flughafen	3.13	n	n
+Gepäcknetz	Staumöglichkeit	2.25	n	n
+Geschirrdurcheinander	Menschenleben	0.5	n	n
+Geschlecht	Mann	3	n	n
+Gewalt	Frieden	2.63	n	n
+Gewalt	Kämpfer	2.63	n	n
+Gewicht	Karriere	0.38	n	n
+Glaube	natürlich	0.5	n	a
+Glück	glücklich	3.88	n	a
+Gorilla	Schlange	1.25	n	n
+großzügig	schrumpfen	0.5	a	v
+gründen	Arbeitsgruppe	0.75	v	n
+Grundlagenforschung	verstehen	1.63	n	v
+Hand	Erwachsener	1.38	n	n
+Hand	Mensch	2.75	n	n
+heimisch	Urwaldhaus	1	a	n
+helfen	unterstützen	3.38	v	v
+herausstreichen	öffentlich	0.5	v	a
+Herkunft	Geschlecht	1.38	n	n
+Hintergrund	Fassade	2	n	n
+Hirn	Gehirn	3.88	n	n
+Hirnsignal	Neuronenaktivität	3.5	n	n
+Hoffnung	Resignation	2.75	n	n
+Honorarbasis	bezahlen	3	n	v
+Hunderttausend	Menge	3	n	n
+Hunger	Armut	2.88	n	n
+Inaugurationsmesse	Premiere	2.13	n	n
+informieren	erfahren	2.63	v	v
+Innenspiegel	Auto	3.13	n	n
+Internetseite	herunterladen	3.25	n	v
+italienisch	vergehen	0	a	v
+Jäger	Wald	2.75	n	n
+Kaffeetasse	parallel	0	n	a
+Kaffeetasse	Tasse	3.75	n	n
+Kamera	TV-Kamera	3.75	n	n
+kämpfen	idyllisch	0.13	v	a
+kämpfen	Veterinär	0.38	v	n
+Karriere	hinaufklettern	2	n	v
+Karriere	Risiko	1	n	n
+Kind	Familie	3.38	n	n
+Kompaktvan	Modell	2.5	n	n
+Kopfairbag	Seitenairbag	3.25	n	n
+Krankheit	reißen	0.25	n	v
+Krebserkennung	Röntgenaufnahme	2	n	n
+kühl	hübsch	0.38	a	a
+Kulturwissenschaft	Grafiker	0.63	n	n
+lachen	leben	1.63	v	v
+lassen	prägen	0.25	v	v
+laufen	bleiben	1.25	v	v
+leben	hellen	0.13	v	v
+leben	Tod	3.25	v	n
+Lebensbedürfnis	ansiedeln	0.38	n	v
+legen	Tisch	1.13	v	n
+lernen	gleichzeitig	0	v	a
+Lied	singen	3.38	n	v
+Linguistik	Wissenschaft	3.5	n	n
+Luft	Leben	2.75	n	n
+Lupe	suchen	2	n	v
+lustig	Witz	3.25	a	n
+machen	anfertigen	3.63	v	v
+machen	ausüben	2.5	v	v
+Macht	Reich	2.5	n	n
+Mai	Januar	2.88	n	n
+Mann	Geschäftspartner	1.5	n	n
+männlich	Weiblich	3.13	a	a
+Marktl	Bayern	2.25	n	n
+Mehrarbeit	Workaholic	2	n	n
+Meinung	Überzeugung	3.13	n	n
+Mercedes	Premium-Hersteller	2.63	n	n
+Minister	Außenminister	3.38	n	n
+Minister	Ministerpräsident	3.38	n	n
+Minister	Politiker	3.25	n	n
+mitteilen	Nachricht	3	v	n
+moderat	extra	1.25	a	a
+modern	sportlich	1.25	a	a
+momentan	kommend	1.38	a	v
+Monate	alt	2.25	n	a
+Montag	November	2.38	n	n
+Motor	Hubraum	2.75	n	n
+nachgehen	untersuchen	2.75	v	v
+Natur	künstlich	2.63	n	a
+Niedersachsen	Landesverband	1.63	n	n
+niederschmetternd	positiv	1.63	a	a
+Objekt	Gegenstand	3.88	n	n
+objektiv	subjektiv	3.13	a	a
+pädagogisch	weitläufig	0.5	a	a
+Papst	Kirche	3.38	n	n
+parallel	linear	1.75	a	a
+Pass	Reiseschutzpass	2.75	n	n
+Petersdom	Inaugurationsmesse	2.63	n	n
+Pinguin	baden	1.5	n	v
+plätschern	Wasser	2.88	v	n
+Platz	aufgebläht	0.13	n	a
+Platz	Petersplatz	3.13	n	n
+Pontifikat	Papst	3.38	n	n
+Post	Portokosten	3	n	n
+Premium-Hersteller	Opel	1.63	n	n
+Premium-Hersteller	VW	2	n	n
+Problem	Schwierigkeit	3.25	n	n
+Projekt	Aktion	2	n	n
+Prozentzeichen	Symbol	3.38	n	n
+Prüfung	Zeugnis	2.5	n	n
+Punktverlust	Platz	1.13	n	n
+Ratzinger	Papst	3.38	n	n
+Relevanz	relevant	3.88	n	a
+riesig	üppig	2.63	a	a
+rot-weiß	weißblau	2.75	a	a
+sachlich	Seriosität	2.13	a	n
+sagen	erklären	2.13	v	v
+sagen	mitteilen	3.13	v	v
+Sandwich-Konzept	Sicherheit	0.5	n	n
+schauen	sehen	3.75	v	v
+Schleusung	Betrugshandlung	2.13	n	n
+schließen	Überlegung	0.88	v	n
+Schrank	Küchenschrank	3.38	n	n
+Schwabe	sparen	2.75	n	v
+Schwabe	Stuttgarter	3.38	n	n
+Seitenansicht	A-Säule	0.88	n	n
+Selbstinszenierung	Beziehungsarbeit	0.5	n	n
+serienmäßig	extra	2.13	a	a
+Sicherheit	Frontalkollision	1.63	n	n
+Sicherheit	klobig	0.25	n	a
+Sohn	aussteigen	0	n	v
+Sohn	Vater	3.38	n	n
+Spitze	allein	1.13	n	a
+Spitze	hoch	2.25	n	a
+sportlich	Interior	0	a	n
+sportlich	teuer	0.38	a	a
+stark	Gehaltsunterschied	0.13	a	n
+stark	Kämpfer	1.88	a	n
+Steckdose	komplex	0.13	n	a
+Steckdose	Stern	0.13	n	n
+Stellenangebot	sehen	0.38	n	v
+Stellenangebot	Wochenzeitung	2.25	n	n
+Stellenanzeige	Bewerbungsgespräch	2.25	n	n
+Stellenanzeige	rasch	0.5	n	a
+Stoiber	drehfreudig	0.25	n	a
+Stoiber	Ministerpräsident	3.13	n	n
+Studie	Dissertationsthema	1.88	n	n
+Studie	Ergebnis	2.75	n	n
+Studierende	Abteilung	1.63	n	n
+Studierende	Note	2.38	n	n
+Studium	arbeiten	2.63	n	v
+Studium	Beruf	3	n	n
+Studium	Deutscher	0.25	n	n
+Studium	Europa	0.5	n	n
+Studium	Gegenstand	0.88	n	n
+Studium	studieren	4	n	v
+suchen	Bundesbürger	0	v	n
+suchen	finden	3	v	v
+Suchmaschinenbetreiber	Eleganz	0.25	n	n
+Suchmaschinenbetreiber	Linkstatistik	1.75	n	n
+Suchstrategie	Optik	0.25	n	n
+Suchstrategie	suchen	3.5	n	v
+summieren	selbstbewusst	0.13	v	a
+summieren	teuer	0.88	v	a
+Tag	demütig	0.25	n	a
+Tag	Donnerstag	3.38	n	n
+Tag	Leben	1.5	n	n
+Tag	Schreibtisch	0	n	n
+Tag	sichtbar	0.63	n	a
+Tag	Stunde	2.75	n	n
+Tastatur	Gott	0	n	n
+Tastatur	Suche	0.63	n	n
+Tätigkeit	Arbeit	3	n	n
+Tätigkeit	visuell	0.13	n	a
+teuer	kostspielig	3.88	a	a
+teuer	Symbol	0.25	a	n
+Tier	Natur	2.63	n	n
+Tier	Röntgenaufnahme	0.25	n	n
+Tierpark	Giraffe	3	n	n
+Tierpark	Grafik	0.5	n	n
+Tod	ähneln	0	n	a
+Tod	Beerdigung	3.25	n	n
+Topmanagement	Job	2.5	n	n
+Topmanagement	Unternehmen	2.75	n	n
+Traurigkeit	bekommen	0.13	n	v
+Traurigkeit	Heimgang	1.13	n	n
+überzeugen	Mitschuld	0.5	v	n
+überzeugen	zeigen	1.5	v	v
+Überzeugung	übertreiben	0.63	n	v
+Überzeugung	Zweifel	2.63	n	n
+Umfrage	Erfolg	0.13	n	n
+Umfrage	Quartalsumfrage	2.88	n	n
+umklappen	Berlin-Kreuzberg	0	v	n
+umklappen	flachlegen	1.63	v	v
+Unternehmen	Dieselversion	0	n	n
+Unternehmen	Firma	3.63	n	n
+untersuchen	Benedikt	0	v	n
+untersuchen	suchen	2.5	v	v
+Untersuchungsausschuss	aussagen	1.88	n	v
+Untersuchungsausschuss	umfassen	0.38	n	v
+Van	Sports-Tourer	2.38	n	n
+Van	Vorschrift	0.25	n	n
+Vatikan	ausgehen	0.13	n	v
+Vatikan	Katholik	3.25	n	n
+veranstalten	betreuen	1.38	v	v
+veranstalten	dauern	0.75	v	v
+verantwortlich	Amt	2.25	a	n
+verantwortlich	zuständig	3.63	a	a
+vergangen	damalig	3.25	a	a
+vergangen	weißblau	0	a	a
+Vergangenheit	alte	2	n	a
+Vergangenheit	Deutschland	1	n	n
+verhindert	Bayerisch	0	a	a
+verhindert	Beihilfe	0.75	a	n
+verkaufen	bezahlen	2.5	v	v
+verkaufen	fahren	0.13	v	v
+Vernehmung	bedrohen	0.75	n	v
+Vernehmung	vernommen	3.63	n	v
+versäumen	sagen	0.13	v	v
+versäumen	überprüfen	0.13	v	v
+verschicken	Post	3	v	n
+verschicken	Rede	0.25	v	n
+versichern	Auftritt	0.13	v	n
+versichern	bedauern	0.5	v	v
+viel	groß	2	a	a
+viel	schreiben	0.38	a	v
+Volierenzelt	Erfolg	0	n	n
+Volierenzelt	Käfig	2.38	n	n
+vorankommen	Entwicklung	2.5	v	n
+vorankommen	Grundpreis	0.25	v	n
+weit	Bewerbung	0	a	n
+weit	erklären	0.25	a	v
+weit	nahe	3.13	a	a
+weit	wegrennen	1.5	a	v
+Welle	Schaden	1	n	n
+Welle	Surfer	3.13	n	n
+Widerspruch	Analyse	1.13	n	n
+Widerspruch	Gebiet	0	n	n
+Wien	deutschsprachig	3	n	a
+Wien	Kopfairbag	0	n	n
+Wirtschaftsprofessor	Irrtum	0.38	n	n
+Wirtschaftsprofessor	Professor	3.63	n	n
+Wirtschaftsuniversität	Abteilung	1.75	n	n
+Wirtschaftsuniversität	historisch	0.63	n	a
+Witz	Gepäckkontrolle	0.25	n	n
+Witz	Joke	4	n	n
+Witz	Kopf	1.13	n	n
+Witz	Makake	0.13	n	n
+Zebra	Stellenanzeige	0	n	n
+Zebra	Tier	3.25	n	n
+Zielstrebigkeit	ablehnen	0.25	n	v
+Zielstrebigkeit	Erfolg	2.63	n	n

WordSim353/data/german_gurevych_zesch/gur_350_reduced.tsv

+#WORD1	WORD2	Value	POS1	POS2
+Absage	ablehnen	3.5	n	v
+Absage	Stellenanzeige	1.88	n	n
+Afrika	historisch	1	n	a
+Agentur	Irrtum	0	n	n
+analysieren	Analyse	3.88	v	n
+Ansehen	Schaden	0.88	n	n
+aufklären	erklären	2.5	v	v
+Aufpreis	Grundpreis	3.13	n	n
+Aufstieg	Erfolg	3.25	n	n
+aufzeichnen	schreiben	2.75	v	v
+Aussage	Auftritt	1.38	n	n
+Aussage	Rede	2.38	n	n
+Aussage	sagen	3.38	n	v
+Aussterben	bedrohen	2.13	n	v
+Auto	fahren	3.5	n	v
+Bayern	Bayerisch	4	n	a
+Bayern	Deutschland	3.5	n	n
+Beamte	Amt	3.63	n	n
+beginnen	dauern	2.38	v	v
+begründen	ausgehen	0.88	v	v
+Behörde	Vorschrift	2.75	n	n
+beinhalten	umfassen	3.25	v	v
+Benedetto	Benedikt	3.63	n	n
+Berufstätigkeit	Erfolg	2.13	n	n
+beschleunigen	übertreiben	1.13	v	v
+beschuldigen	Mitschuld	2.5	v	n
+Besucher	bekommen	1.38	n	v
+Bewerbung	Job	2.38	n	n
+Bild	ähneln	1.38	n	v
+Bild	Grafik	3.13	n	n
+Bild	Röntgenaufnahme	3	n	n
+Bild	Symbol	2.13	n	n
+Bild	visuell	3	n	a
+Böse	Gott	2	n	n
+Botschaft	sichtbar	0.25	n	a
+Büro	Schreibtisch	3	n	n
+Demut	demütig	4	n	a
+demütig	selbstbewusst	1.88	a	a
+Design	Optik	2.63	n	n
+Designer	Eleganz	2.63	n	n
+deutsch	Deutscher	3.88	a	n
+Deutscher	Bundesbürger	3.5	n	n
+Deutschland	Europa	3.25	n	n
+Ding	Gegenstand	4	n	n
+Doktorandin	Abteilung	1.88	n	n
+Doktorandin	Dissertationsthema	2.63	n	n
+dringend	rasch	2.38	a	a
+Durchsicht	sehen	2.75	n	v
+einfach	komplex	2.75	a	a
+Einrichtung	Interior	3.5	n	n
+Einsamkeit	allein	3.5	n	a
+einsteigen	aussteigen	2.75	v	v
+Eleganz	klobig	1.38	n	a
+Eltern	Vater	3.5	n	n
+entgehen	bewundern	0.13	v	v
+Erfolg	erfolgreich	4	n	a
+Erfolg	Maßstab	1.25	n	n
+erforschen	herausfinden	3.13	v	v
+Erhalt	bedroht	1	n	a
+erkennen	sehen	3	v	v
+erklären	begründen	2.5	v	v
+erklären	machen	0.5	v	v
+ernst	ironisch	2	a	a
+erst	Ursprungsort	1.38	a	n
+Erwachsener	Geist	0	n	n
+Erwachsener	Kinder	2.63	n	n
+erwarten	klären	0	v	v
+fahren	Automobil	3	v	n
+filtern	herausfiltern	3.63	v	v
+filtern	selektieren	3.38	v	v
+finden	herausfinden	3	v	v
+Fisch	schwimmen	3.38	n	v
+fokussieren	Aufmerksamkeit	2.63	v	n
+folgen	sortieren	0.25	v	v
+Form	Farbe	2.13	n	n
+formulieren	Formulierung	3.88	v	n
+Formulierung	Stiftung	0.13	n	n
+Forscher	Wissenschaftler	3.88	n	n
+Frage	Antwort	3.25	n	n
+Franzose	Deutscher	2.38	n	n
+Frau	Familie	2.75	n	n
+Frau	Mann	3.25	n	n
+Frust	Rache	1.88	n	n
+geben	nehmen	3.25	v	v
+Gefühl	Frau	1.75	n	n
+Gegenwind	kritisieren	0.5	n	v
+Gehege	Zoo	2.63	n	n
+Gehirn	Kortex	3.25	n	n
+Gehirn	verstehen	2.13	n	v
+gemeinsam	leben	1	a	v
+Generation	Jugendlicher	2.5	n	n
+geografisch	praktisch	0.13	a	a
+Geschlecht	Mann	3	n	n
+Gewalt	Frieden	2.63	n	n
+Gewalt	Kämpfer	2.63	n	n
+Gewicht	Karriere	0.38	n	n
+Glaube	natürlich	0.5	n	a
+Glück	glücklich	3.88	n	a
+Gorilla	Schlange	1.25	n	n
+großzügig	schrumpfen	0.5	a	v
+gründen	Arbeitsgruppe	0.75	v	n
+Grundlagenforschung	verstehen	1.63	n	v
+Hand	Erwachsener	1.38	n	n
+Hand	Mensch	2.75	n	n
+helfen	unterstützen	3.38	v	v
+Herkunft	Geschlecht	1.38	n	n
+Hintergrund	Fassade	2	n	n
+Hirn	Gehirn	3.88	n	n
+Hoffnung	Resignation	2.75	n	n
+Honorarbasis	bezahlen	3	n	v
+Hunderttausend	Menge	3	n	n
+Hunger	Armut	2.88	n	n
+informieren	erfahren	2.63	v	v
+Innenspiegel	Auto	3.13	n	n
+Internetseite	herunterladen	3.25	n	v
+italienisch	vergehen	0	a	v
+Jäger	Wald	2.75	n	n
+kämpfen	idyllisch	0.13	v	a
+kämpfen	Veterinär	0.38	v	n
+Karriere	Risiko	1	n	n
+Kind	Familie	3.38	n	n
+Kompaktvan	Modell	2.5	n	n
+Krankheit	reißen	0.25	n	v
+kühl	hübsch	0.38	a	a
+Kulturwissenschaft	Grafiker	0.63	n	n
+lachen	leben	1.63	v	v
+lassen	prägen	0.25	v	v
+laufen	bleiben	1.25	v	v
+leben	hellen	0.13	v	v
+leben	Tod	3.25	v	n
+legen	Tisch	1.13	v	n
+lernen	gleichzeitig	0	v	a
+Lied	singen	3.38	n	v
+Linguistik	Wissenschaft	3.5	n	n
+Luft	Leben	2.75	n	n
+Lupe	suchen	2	n	v
+lustig	Witz	3.25	a	n
+machen	anfertigen	3.63	v	v
+machen	ausüben	2.5	v	v
+Macht	Reich	2.5	n	n
+Mai	Januar	2.88	n	n
+Mann	Geschäftspartner	1.5	n	n
+männlich	Weiblich	3.13	a	a
+Marktl	Bayern	2.25	n	n
+Mehrarbeit	Workaholic	2	n	n
+Meinung	Überzeugung	3.13	n	n
+Minister	Außenminister	3.38	n	n
+Minister	Ministerpräsident	3.38	n	n
+Minister	Politiker	3.25	n	n
+mitteilen	Nachricht	3	v	n
+moderat	extra	1.25	a	a
+modern	sportlich	1.25	a	a
+momentan	kommend	1.38	a	v
+Monate	alt	2.25	n	a
+Montag	November	2.38	n	n
+Motor	Hubraum	2.75	n	n
+nachgehen	untersuchen	2.75	v	v
+Natur	künstlich	2.63	n	a
+Niedersachsen	Landesverband	1.63	n	n
+Objekt	Gegenstand	3.88	n	n
+objektiv	subjektiv	3.13	a	a
+pädagogisch	weitläufig	0.5	a	a
+Papst	Kirche	3.38	n	n
+parallel	linear	1.75	a	a
+Pinguin	baden	1.5	n	v
+Platz	aufgebläht	0.13	n	a
+Platz	Petersplatz	3.13	n	n
+Pontifikat	Papst	3.38	n	n
+Problem	Schwierigkeit	3.25	n	n
+Projekt	Aktion	2	n	n
+Prüfung	Zeugnis	2.5	n	n
+Punktverlust	Platz	1.13	n	n
+Ratzinger	Papst	3.38	n	n
+Relevanz	relevant	3.88	n	a
+riesig	üppig	2.63	a	a
+sachlich	Seriosität	2.13	a	n
+sagen	erklären	2.13	v	v
+sagen	mitteilen	3.13	v	v
+schauen	sehen	3.75	v	v
+schließen	Überlegung	0.88	v	n
+Schwabe	sparen	2.75	n	v
+Schwabe	Stuttgarter	3.38	n	n
+serienmäßig	extra	2.13	a	a
+Sicherheit	klobig	0.25	n	a
+Sohn	aussteigen	0	n	v
+Sohn	Vater	3.38	n	n
+Spitze	allein	1.13	n	a
+Spitze	hoch	2.25	n	a
+sportlich	Interior	0	a	n
+sportlich	teuer	0.38	a	a
+stark	Kämpfer	1.88	a	n
+Steckdose	komplex	0.13	n	a
+Steckdose	Stern	0.13	n	n
+Stellenangebot	sehen	0.38	n	v
+Stellenangebot	Wochenzeitung	2.25	n	n
+Stoiber	Ministerpräsident	3.13	n	n
+Studie	Dissertationsthema	1.88	n	n
+Studie	Ergebnis	2.75	n	n
+Studierende	Abteilung	1.63	n	n
+Studierende	Note	2.38	n	n
+Studium	arbeiten	2.63	n	v
+Studium	Beruf	3	n	n
+Studium	Deutscher	0.25	n	n
+Studium	Europa	0.5	n	n
+Studium	Gegenstand	0.88	n	n
+Studium	studieren	4	n	v
+suchen	Bundesbürger	0	v	n
+suchen	finden	3	v	v
+summieren	selbstbewusst	0.13	v	a
+summieren	teuer	0.88	v	a
+Tag	demütig	0.25	n	a
+Tag	Donnerstag	3.38	n	n
+Tag	Leben	1.5	n	n
+Tag	Schreibtisch	0	n	n
+Tag	sichtbar	0.63	n	a
+Tag	Stunde	2.75	n	n
+Tastatur	Gott	0	n	n
+Tastatur	Suche	0.63	n	n
+Tätigkeit	Arbeit	3	n	n
+Tätigkeit	visuell	0.13	n	a
+teuer	kostspielig	3.88	a	a
+teuer	Symbol	0.25	a	n
+Tier	Natur	2.63	n	n
+Tier	Röntgenaufnahme	0.25	n	n
+Tierpark	Giraffe	3	n	n
+Tierpark	Grafik	0.5	n	n
+Tod	ähneln	0	n	a
+Tod	Beerdigung	3.25	n	n
+Traurigkeit	bekommen	0.13	n	v
+überzeugen	Mitschuld	0.5	v	n
+überzeugen	zeigen	1.5	v	v
+Überzeugung	übertreiben	0.63	n	v
+Überzeugung	Zweifel	2.63	n	n
+Umfrage	Erfolg	0.13	n	n
+Unternehmen	Firma	3.63	n	n
+untersuchen	Benedikt	0	v	n
+untersuchen	suchen	2.5	v	v
+Untersuchungsausschuss	aussagen	1.88	n	v
+Untersuchungsausschuss	umfassen	0.38	n	v
+Van	Vorschrift	0.25	n	n
+Vatikan	ausgehen	0.13	n	v
+Vatikan	Katholik	3.25	n	n
+veranstalten	betreuen	1.38	v	v
+veranstalten	dauern	0.75	v	v
+verantwortlich	Amt	2.25	a	n
+verantwortlich	zuständig	3.63	a	a
+vergangen	damalig	3.25	a	a
+Vergangenheit	alte	2	n	a
+Vergangenheit	Deutschland	1	n	n
+verhindert	Bayerisch	0	a	a
+verhindert	Beihilfe	0.75	a	n
+verkaufen	bezahlen	2.5	v	v
+verkaufen	fahren	0.13	v	v
+Vernehmung	bedrohen	0.75	n	v
+Vernehmung	vernommen	3.63	n	v
+versäumen	sagen	0.13	v	v
+versäumen	überprüfen	0.13	v	v
+verschicken	Post	3	v	n
+verschicken	Rede	0.25	v	n
+versichern	Auftritt	0.13	v	n
+versichern	bedauern	0.5	v	v
+viel	groß	2	a	a
+viel	schreiben	0.38	a	v
+vorankommen	Entwicklung	2.5	v	n
+vorankommen	Grundpreis	0.25	v	n
+weit	Bewerbung	0	a	n
+weit	erklären	0.25	a	v
+weit	nahe	3.13	a	a
+Welle	Schaden	1	n	n
+Welle	Surfer	3.13	n	n
+Widerspruch	Analyse	1.13	n	n
+Widerspruch	Gebiet	0	n	n
+Wien	deutschsprachig	3	n	a
+Wirtschaftsprofessor	Irrtum	0.38	n	n
+Wirtschaftsprofessor	Professor	3.63	n	n
+Wirtschaftsuniversität	Abteilung	1.75	n	n
+Wirtschaftsuniversität	historisch	0.63	n	a
+Witz	Joke	4	n	n
+Witz	Kopf	1.13	n	n
+Zebra	Stellenanzeige	0	n	n
+Zebra	Tier	3.25	n	n
+Zielstrebigkeit	ablehnen	0.25	n	v
+Zielstrebigkeit	Erfolg	2.63	n	n

WordSim353/data/multilingual_leviant_reichart/wordsim353-english-rel.txt

+Word1	Word2	Score
+love	sex	8.31
+book	paper	7.69
+computer	keyboard	8.38
+computer	internet	8.23
+telephone	communication	8.38
+drug	abuse	6.46
+smart	student	6.58
+company	stock	6.85
+stock	market	7.46
+stock	phone	1.54
+stock	CD	4.04
+stock	jaguar	1.54
+stock	egg	1.62
+fertility	egg	8.38
+stock	live	3.69
+stock	life	1.85
+book	library	8.77
+bank	money	8.69
+professor	cucumber	0.23
+king	cabbage	1.15
+Jerusalem	Israel	8.77
+Jerusalem	Palestinian	7.85
+holy	sex	1.46
+Maradona	football	4.92
+tennis	racket	8.69
+Arafat	peace	3.62
+Arafat	terror	5.92
+law	lawyer	9.23
+movie	star	7.46
+movie	popcorn	6.77
+movie	critic	6.08
+movie	theater	7.85
+physics	proton	6.38
+space	chemistry	3.77
+alcohol	chemistry	4.62
+drink	car	0.77
+drink	ear	0.38
+drink	mouth	6.38
+baby	mother	6.23
+drink	mother	1.31
+tool	implement	7.00
+brother	monk	5.31
+crane	implement	3.08
+lad	brother	5.62
+journey	car	5.62
+monk	oracle	4.31
+cemetery	woodland	2.15
+coast	hill	3.15
+forest	graveyard	2.00
+shore	woodland	3.00
+monk	slave	2.15
+coast	forest	2.92
+lad	wizard	1.85
+chord	smile	1.23
+glass	magician	1.15
+noon	string	0.46
+rooster	voyage	0.54
+money	wealth	8.38
+money	property	5.38
+money	possession	6.46
+money	bank	7.23
+money	deposit	7.46
+money	withdrawal	7.38
+money	laundering	6.54
+money	operation	3.00
+tiger	zoo	7.65
+psychology	anxiety	7.27
+psychology	fear	5.96
+psychology	depression	7.31
+psychology	clinic	7.46
+psychology	doctor	7.98
+psychology	Freud	8.46
+psychology	mind	8.46
+psychology	health	7.35
+psychology	cognition	7.50
+planet	constellation	7.08
+planet	galaxy	7.65
+planet	space	8.23
+planet	astronomer	7.23
+precedent	information	5.54
+precedent	cognition	4.15
+precedent	law	6.54
+precedent	collection	3.92
+precedent	group	2.38
+cup	coffee	7.65
+cup	article	1.85
+cup	drink	7.96
+cup	food	4.15
+cup	substance	3.69
+cup	liquid	6.82
+energy	secretary	0.92
+secretary	senate	3.92
+energy	laboratory	4.12
+computer	laboratory	4.58
+weapon	secret	4.73
+FBI	fingerprint	5.38
+FBI	investigation	8.15
+investigation	effort	5.50
+Mars	water	2.65
+Mars	scientist	4.31
+news	report	8.15
+canyon	landscape	7.62
+image	surface	4.04
+discovery	space	6.50
+water	seepage	6.15
+sign	recess	1.42
+Wednesday	news	1.96
+computer	news	4.08
+territory	surface	3.96
+atmosphere	landscape	3.58
+president	medal	3.38
+war	troops	7.88
+record	number	5.88
+theater	history	2.65
+volunteer	motto	1.85
+prejudice	recognition	2.73
+decoration	valor	6.88
+century	nation	1.46
+delay	racism	1.08
+delay	news	1.88
+minister	party	2.58
+peace	plan	3.69
+minority	peace	2.85
+attempt	peace	2.35
+government	crisis	4.42
+deployment	departure	7.81
+deployment	withdrawal	6.27
+energy	crisis	3.85
+announcement	effort	3.08
+stroke	hospital	5.88
+disability	death	5.15
+victim	emergency	6.77
+treatment	recovery	7.54
+journal	association	3.00
+doctor	liability	4.69
+liability	insurance	8.48
+school	center	5.08
+reason	hypertension	0.69
+reason	criterion	5.50
+hundred	percent	6.69
+death	row	5.46
+death	inmate	3.92
+lawyer	evidence	7.35
+life	term	5.35
+word	similarity	2.00
+board	recommendation	2.50
+governor	interview	1.31
+OPEC	country	1.92
+peace	atmosphere	2.31
+peace	insurance	0.69
+territory	kilometer	2.46
+competition	price	4.54
+consumer	confidence	4.08
+consumer	energy	4.50
+problem	airport	1.92
+car	flight	3.46
+credit	card	6.38
+credit	information	5.65
+hotel	reservation	7.59
+grocery	money	4.88
+registration	arrangement	5.12
+arrangement	accommodation	6.54
+month	hotel	0.31
+arrival	hotel	4.27
+bed	closet	2.33
+closet	clothes	7.69
+situation	conclusion	3.46
+situation	isolation	1.88
+impartiality	interest	2.65
+direction	combination	0.81
+street	children	1.54
+listing	proximity	1.96
+listing	category	6.29
+production	hike	1.00
+benchmark	index	4.46
+media	trading	1.15
+media	gain	1.31
+dividend	calculation	6.62
+currency	market	5.46
+OPEC	oil	7.52
+oil	stock	5.69
+announcement	production	2.38
+announcement	warning	6.15
+profit	warning	0.77
+dollar	profit	6.77
+dollar	loss	5.38
+computer	software	8.00
+network	hardware	7.73
+equipment	maker	3.62
+luxury	car	5.12
+five	month	1.08
+report	gain	3.00
+investor	earning	6.42
+baseball	season	5.31
+game	victory	6.54
+game	team	7.15
+game	series	4.62
+game	defeat	5.38
+seven	series	2.38
+seafood	sea	7.94
+food	preparation	5.46
+video	archive	3.69
+start	year	1.42
+start	match	2.08
+game	round	4.46
+boxing	round	6.54
+fighting	defeating	5.31
+line	insurance	1.15
+day	summer	3.54
+summer	drought	5.19
+summer	nature	4.62
+day	dawn	7.62
+nature	environment	8.85
+environment	ecology	8.23
+nature	man	6.69
+soap	opera	5.38
+life	lesson	5.69
+focus	life	4.46
+production	crew	5.38
+television	film	6.88
+lover	quarrel	5.69
+viewer	serial	3.04
+possibility	girl	2.46
+population	development	4.42
+morality	importance	4.46
+morality	marriage	4.77
+gender	equality	6.27
+change	attitude	5.54
+family	planning	5.69
+opera	industry	3.38
+sugar	approach	1.00
+practice	institution	3.31
+ministry	culture	4.35
+problem	challenge	7.81
+size	prominence	6.19
+country	citizen	6.15
+planet	people	5.85
+development	issue	4.58
+experience	music	4.54
+music	project	4.46
+chance	credibility	4.12
+exhibit	memorabilia	5.85
+concert	virtuoso	6.04
+observation	architecture	3.62
+space	world	5.85
+preservation	world	4.31
+admission	ticket	7.42
+shower	flood	6.19
+weather	forecast	7.88
+disaster	area	5.04
+governor	office	5.85
+architecture	century	3.31
\ No newline at end of file

WordSim353/data/multilingual_leviant_reichart/wordsim353-english-sim.txt

+Word1	Word2	Score
+tiger	cat	8.31
+tiger	tiger	10.00
+plane	car	5.85
+train	car	6.31
+television	radio	6.85
+media	radio	8.08
+bread	butter	7.54
+cucumber	potato	5.92
+doctor	nurse	8.15
+professor	doctor	5.31
+student	professor	6.54
+smart	student	6.58
+smart	stupid	5.92
+stock	phone	1.54
+stock	CD	4.04
+stock	jaguar	1.54
+stock	egg	1.62
+stock	live	3.69
+stock	life	1.85
+wood	forest	8.38
+money	cash	9.38
+professor	cucumber	0.23
+king	cabbage	1.15
+king	queen	8.46
+king	rook	5.92
+bishop	rabbi	8.23
+holy	sex	1.46
+fuck	sex	9.31
+football	basketball	7.08
+football	tennis	6.46
+Arafat	Jackson	2.77
+physics	chemistry	7.77
+space	chemistry	3.77
+vodka	gin	8.15
+vodka	brandy	7.85
+drink	car	0.77
+drink	ear	0.38
+drink	eat	6.61
+drink	mother	1.31
+car	automobile	9.54
+gem	jewel	9.31
+journey	voyage	9.54
+boy	lad	9.46
+asylum	madhouse	9.00
+magician	wizard	8.77
+furnace	stove	7.69
+food	fruit	8.15
+bird	cock	6.85
+bird	crane	8.31
+crane	implement	3.08
+lad	brother	5.62
+monk	oracle	4.31
+cemetery	woodland	2.15
+food	rooster	2.92
+coast	hill	3.15
+forest	graveyard	2.00
+shore	woodland	3.00
+monk	slave	2.15
+coast	forest	2.92
+lad	wizard	1.85
+chord	smile	1.23
+glass	magician	1.15
+noon	string	0.46
+rooster	voyage	0.54
+money	dollar	9.08
+money	cash	9.38
+money	currency	9.54
+money	operation	3.00
+tiger	jaguar	8.42
+tiger	feline	8.46
+tiger	carnivore	8.92
+tiger	mammal	8.81
+tiger	animal	9.06
+tiger	organism	6.77
+tiger	fauna	4.31
+psychology	psychiatry	8.35
+psychology	science	6.81
+psychology	discipline	6.23
+planet	star	8.08
+planet	moon	8.15
+planet	sun	8.58
+precedent	example	6.85
+precedent	information	5.54
+precedent	cognition	4.15
+precedent	collection	3.92
+precedent	group	2.38
+precedent	antecedent	6.69
+cup	tableware	7.54
+cup	article	1.85
+cup	artifact	3.54
+cup	object	6.62
+cup	entity	2.08
+cup	food	4.15
+cup	substance	3.69
+jaguar	cat	8.85
+jaguar	car	7.35
+energy	secretary	0.92
+investigation	effort	5.50
+Mars	water	2.65
+image	surface	4.04
+sign	recess	1.42
+Wednesday	news	1.96
+mile	kilometer	6.92
+computer	news	4.08
+atmosphere	landscape	3.58
+president	medal	3.38
+skin	eye	4.69
+Japanese	American	6.15
+theater	history	2.65
+volunteer	motto	1.85
+prejudice	recognition	2.73
+century	year	6.54
+century	nation	1.46
+delay	racism	1.08
+delay	news	1.88
+peace	plan	3.69
+minority	peace	2.85
+attempt	peace	2.35
+deployment	departure	7.81
+announcement	news	7.62
+announcement	effort	3.08
+journal	association	3.00
+doctor	personnel	5.73
+school	center	5.08
+reason	hypertension	0.69
+Harvard	Yale	8.77
+hospital	infrastructure	5.42
+life	death	6.96
+life	term	5.35
+word	similarity	2.00
+board	recommendation	2.50
+governor	interview	1.31
+peace	atmosphere	2.31
+peace	insurance	0.69
+travel	activity	4.85
+consumer	confidence	4.08
+consumer	energy	4.50
+problem	airport	1.92
+car	flight	3.46
+month	hotel	0.31
+type	kind	9.31
+situation	conclusion	3.46
+situation	isolation	1.88
+direction	combination	0.81
+street	place	6.08
+street	avenue	8.77
+street	block	7.85
+street	children	1.54
+listing	proximity	1.96
+cell	phone	8.54
+production	hike	1.00
+benchmark	index	4.46
+media	trading	1.15
+media	gain	1.31
+dividend	payment	8.38
+calculation	computation	9.38
+announcement	production	2.38
+profit	warning	0.77
+profit	loss	6.69
+dollar	yen	8.08
+dollar	buck	9.54
+phone	equipment	6.85
+five	month	1.08
+report	gain	3.00
+liquid	water	8.62
+marathon	sprint	5.69
+seven	series	2.38
+seafood	food	8.52
+seafood	lobster	8.60
+lobster	food	8.37
+lobster	wine	2.54
+start	year	1.42
+start	match	2.08
+championship	tournament	7.92
+line	insurance	1.15
+man	woman	8.38
+man	governor	6.00
+murder	manslaughter	7.65
+opera	performance	7.46
+focus	life	4.46
+viewer	serial	3.04
+possibility	girl	2.46
+population	development	4.42
+morality	importance	4.46
+morality	marriage	4.77
+Mexico	Brazil	6.19
+opera	industry	3.38
+sugar	approach	1.00
+practice	institution	3.31
+ministry	culture	4.35
+development	issue	4.58
+experience	music	4.54
+music	project	4.46
+glass	metal	4.58
+aluminum	metal	7.62
+chance	credibility	4.12
+rock	jazz	6.85
+museum	theater	6.00
+observation	architecture	3.62
+shower	thunderstorm	6.73
+architecture	century	3.31
\ No newline at end of file

WordSim353/data/multilingual_leviant_reichart/wordsim353-english.txt

+Word1	Word2	Score
+love	sex	8.31
+tiger	cat	8.31
+tiger	tiger	10.00
+book	paper	7.69
+computer	keyboard	8.38
+computer	internet	8.23
+plane	car	5.85
+train	car	6.31
+telephone	communication	8.38
+television	radio	6.85
+media	radio	8.08
+drug	abuse	6.46
+bread	butter	7.54
+cucumber	potato	5.92
+doctor	nurse	8.15
+professor	doctor	5.31
+student	professor	6.54
+smart	student	6.58
+smart	stupid	5.92
+company	stock	6.85
+stock	market	7.46
+stock	phone	1.54
+stock	CD	4.04
+stock	jaguar	1.54
+stock	egg	1.62
+fertility	egg	8.38
+stock	live	3.69
+stock	life	1.85
+book	library	8.77
+bank	money	8.69
+wood	forest	8.38
+money	cash	9.38
+professor	cucumber	0.23
+king	cabbage	1.15
+king	queen	8.46
+king	rook	5.92
+bishop	rabbi	8.23
+Jerusalem	Israel	8.77
+Jerusalem	Palestinian	7.85
+holy	sex	1.46
+fuck	sex	9.31
+Maradona	football	4.92
+football	basketball	7.08
+football	tennis	6.46
+tennis	racket	8.69
+Arafat	peace	3.62
+Arafat	terror	5.92
+Arafat	Jackson	2.77
+law	lawyer	9.23
+movie	star	7.46
+movie	popcorn	6.77
+movie	critic	6.08
+movie	theater	7.85
+physics	proton	6.38
+physics	chemistry	7.77
+space	chemistry	3.77
+alcohol	chemistry	4.62
+vodka	gin	8.15
+vodka	brandy	7.85
+drink	car	0.77
+drink	ear	0.38
+drink	mouth	6.38
+drink	eat	6.61
+baby	mother	6.23
+drink	mother	1.31
+car	automobile	9.54
+gem	jewel	9.31
+journey	voyage	9.54
+boy	lad	9.46
+asylum	madhouse	9.00
+magician	wizard	8.77
+furnace	stove	7.69
+food	fruit	8.15
+bird	cock	6.85
+bird	crane	8.31
+tool	implement	7.00
+brother	monk	5.31
+crane	implement	3.08
+lad	brother	5.62
+journey	car	5.62
+monk	oracle	4.31
+cemetery	woodland	2.15
+food	rooster	2.92
+coast	hill	3.15
+forest	graveyard	2.00
+shore	woodland	3.00
+monk	slave	2.15
+coast	forest	2.92
+lad	wizard	1.85
+chord	smile	1.23
+glass	magician	1.15
+noon	string	0.46
+rooster	voyage	0.54
+money	dollar	9.08
+money	cash	9.38
+money	currency	9.54
+money	wealth	8.38
+money	property	5.38
+money	possession	6.46
+money	bank	7.23
+money	deposit	7.46
+money	withdrawal	7.38
+money	laundering	6.54
+money	operation	3.00
+tiger	jaguar	8.42
+tiger	feline	8.46
+tiger	carnivore	8.92
+tiger	mammal	8.81
+tiger	animal	9.06
+tiger	organism	6.77
+tiger	fauna	4.31
+tiger	zoo	7.65
+psychology	psychiatry	8.35
+psychology	anxiety	7.27
+psychology	fear	5.96
+psychology	depression	7.31
+psychology	clinic	7.46
+psychology	doctor	7.98
+psychology	Freud	8.46
+psychology	mind	8.46
+psychology	health	7.35
+psychology	science	6.81
+psychology	discipline	6.23
+psychology	cognition	7.50
+planet	star	8.08
+planet	constellation	7.08
+planet	moon	8.15
+planet	sun	8.58
+planet	galaxy	7.65
+planet	space	8.23
+planet	astronomer	7.23
+precedent	example	6.85
+precedent	information	5.54
+precedent	cognition	4.15
+precedent	law	6.54
+precedent	collection	3.92
+precedent	group	2.38
+precedent	antecedent	6.69
+cup	coffee	7.65
+cup	tableware	7.54
+cup	article	1.85
+cup	artifact	3.54
+cup	object	6.62
+cup	entity	2.08
+cup	drink	7.96
+cup	food	4.15
+cup	substance	3.69
+cup	liquid	6.82
+jaguar	cat	8.85
+jaguar	car	7.35
+energy	secretary	0.92
+secretary	senate	3.92
+energy	laboratory	4.12
+computer	laboratory	4.58
+weapon	secret	4.73
+FBI	fingerprint	5.38
+FBI	investigation	8.15
+investigation	effort	5.50
+Mars	water	2.65
+Mars	scientist	4.31
+news	report	8.15
+canyon	landscape	7.62
+image	surface	4.04
+discovery	space	6.50
+water	seepage	6.15
+sign	recess	1.42
+Wednesday	news	1.96
+mile	kilometer	6.92
+computer	news	4.08
+territory	surface	3.96
+atmosphere	landscape	3.58
+president	medal	3.38
+war	troops	7.88
+record	number	5.88
+skin	eye	4.69
+Japanese	American	6.15
+theater	history	2.65
+volunteer	motto	1.85
+prejudice	recognition	2.73
+decoration	valor	6.88
+century	year	6.54
+century	nation	1.46
+delay	racism	1.08
+delay	news	1.88
+minister	party	2.58
+peace	plan	3.69
+minority	peace	2.85
+attempt	peace	2.35
+government	crisis	4.42
+deployment	departure	7.81
+deployment	withdrawal	6.27
+energy	crisis	3.85
+announcement	news	7.62
+announcement	effort	3.08
+stroke	hospital	5.88
+disability	death	5.15
+victim	emergency	6.77
+treatment	recovery	7.54
+journal	association	3.00
+doctor	personnel	5.73
+doctor	liability	4.69
+liability	insurance	8.48
+school	center	5.08
+reason	hypertension	0.69
+reason	criterion	5.50
+hundred	percent	6.69
+Harvard	Yale	8.77
+hospital	infrastructure	5.42
+death	row	5.46
+death	inmate	3.92
+lawyer	evidence	7.35
+life	death	6.96
+life	term	5.35
+word	similarity	2.00
+board	recommendation	2.50
+governor	interview	1.31
+OPEC	country	1.92
+peace	atmosphere	2.31
+peace	insurance	0.69
+territory	kilometer	2.46
+travel	activity	4.85
+competition	price	4.54
+consumer	confidence	4.08
+consumer	energy	4.50
+problem	airport	1.92
+car	flight	3.46
+credit	card	6.38
+credit	information	5.65
+hotel	reservation	7.59
+grocery	money	4.88
+registration	arrangement	5.12
+arrangement	accommodation	6.54
+month	hotel	0.31
+type	kind	9.31
+arrival	hotel	4.27
+bed	closet	2.33
+closet	clothes	7.69
+situation	conclusion	3.46
+situation	isolation	1.88
+impartiality	interest	2.65
+direction	combination	0.81
+street	place	6.08
+street	avenue	8.77
+street	block	7.85
+street	children	1.54
+listing	proximity	1.96
+listing	category	6.29
+cell	phone	8.54
+production	hike	1.00
+benchmark	index	4.46
+media	trading	1.15
+media	gain	1.31
+dividend	payment	8.38
+dividend	calculation	6.62
+calculation	computation	9.38
+currency	market	5.46
+OPEC	oil	7.52
+oil	stock	5.69
+announcement	production	2.38
+announcement	warning	6.15
+profit	warning	0.77
+profit	loss	6.69
+dollar	yen	8.08
+dollar	buck	9.54
+dollar	profit	6.77
+dollar	loss	5.38
+computer	software	8.00
+network	hardware	7.73
+phone	equipment	6.85
+equipment	maker	3.62
+luxury	car	5.12
+five	month	1.08
+report	gain	3.00
+investor	earning	6.42
+liquid	water	8.62
+baseball	season	5.31
+game	victory	6.54
+game	team	7.15
+marathon	sprint	5.69
+game	series	4.62
+game	defeat	5.38
+seven	series	2.38
+seafood	sea	7.94
+seafood	food	8.52
+seafood	lobster	8.60
+lobster	food	8.37
+lobster	wine	2.54
+food	preparation	5.46
+video	archive	3.69
+start	year	1.42
+start	match	2.08
+game	round	4.46
+boxing	round	6.54
+championship	tournament	7.92
+fighting	defeating	5.31
+line	insurance	1.15
+day	summer	3.54
+summer	drought	5.19
+summer	nature	4.62
+day	dawn	7.62
+nature	environment	8.85
+environment	ecology	8.23
+nature	man	6.69
+man	woman	8.38
+man	governor	6.00
+murder	manslaughter	7.65
+soap	opera	5.38
+opera	performance	7.46
+life	lesson	5.69
+focus	life	4.46
+production	crew	5.38
+television	film	6.88
+lover	quarrel	5.69
+viewer	serial	3.04
+possibility	girl	2.46
+population	development	4.42
+morality	importance	4.46
+morality	marriage	4.77
+Mexico	Brazil	6.19
+gender	equality	6.27
+change	attitude	5.54
+family	planning	5.69
+opera	industry	3.38
+sugar	approach	1.00
+practice	institution	3.31
+ministry	culture	4.35
+problem	challenge	7.81
+size	prominence	6.19
+country	citizen	6.15
+planet	people	5.85
+development	issue	4.58
+experience	music	4.54
+music	project	4.46
+glass	metal	4.58
+aluminum	metal	7.62
+chance	credibility	4.12
+exhibit	memorabilia	5.85
+concert	virtuoso	6.04
+rock	jazz	6.85
+museum	theater	6.00
+observation	architecture	3.62
+space	world	5.85
+preservation	world	4.31
+admission	ticket	7.42
+shower	thunderstorm	6.73
+shower	flood	6.19
+weather	forecast	7.88
+disaster	area	5.04
+governor	office	5.85
+architecture	century	3.31
\ No newline at end of file

WordSim353/data/multilingual_leviant_reichart/wordsim353-german-rel.txt

+Word1	Word2	Score
+Liebe	Sex	8.46
+Buch	Papier	7.08
+Computer	Tastatur	8.00
+Computer	Internet	8.08
+Telefon	Kommunikation	8.38
+Drogen	Mißbrauch	6.46
+klug	Student	4.85
+Unternehmen	Aktie	6.54
+Aktie	Börse	8.85
+Vorrat	Telefon	0.31
+Vorrat	CD	0.54
+Vorrat	Jaguar	0.23
+Vorrat	Ei	2.15
+Fruchtbarkeit	Ei	7.92
+Aktie	Live	1.08
+Aktie	Leben	0.62
+Buch	Bibliothek	8.31
+Bank	Geld	8.15
+Professor	Gurke	0.15
+König	Kohl	0.23
+Jerusalem	Israel	8.85
+Jerusalem	Palestinensisch	6.85
+Heilig	Sex	0.69
+Maradona	Fußball	8.00
+Tennis	Schläger	7.08
+Arafat	Frieden	2.46
+Arafat	Terror	5.23
+Gesetz	Anwalt	8.38
+Film	Star	7.62
+Film	Popcorn	6.08
+Film	Kritik	5.85
+Kino	Theater	6.85
+Physik	Proton	7.00
+Weltall	Chemie	3.31
+Alkohol	Chemie	5.08
+Drink	Auto	1.85
+Trinken	Ohren	0.62
+Trinken	Mund	6.46
+Baby	Mutter	7.85
+Säugen	Mutter	7.69
+Werkzeug	Arbeitsgerät	8.38
+Bruder	Mönch	5.92
+Kran	Arbeitsgerät	6.08
+Bursche	Bruder	4.23
+Fahrt	Auto	6.62
+Mönch	Orakel	1.38
+Friedhof	Waldgebiet	1.92
+Küste	Hügel	2.69
+Wald	Friedhof	3.31
+Ufer	Waldgebiet	2.31
+Mönch	Sklave	1.08
+Küste	Wald	1.77
+Bursche	Zauberer	0.77
+Akkord	Lächeln	0.31
+Glas	Magier	1.69
+Mittag	Faden	0.15
+Hahn	Reise	0.31
+Geld	Reichtum	8.19
+Geld	Eigentum	6.62
+Geld	Besitz	6.92
+Geld	Bank	8.31
+Geld	Pfand	5.13
+Geld	Einzahlung	6.23
+Geld	Abheben	6.54
+Geld	Wäsche	3.62
+Tiger	Zoo	5.91
+Psychologie	Beklemmung	4.35
+Psychologie	Angst	4.92
+Psychologie	Depression	6.77
+Psychologie	Klinik	6.17
+Psychologie	Arzt	5.85
+Psychologie	Freud	7.00
+Psychologie	Seele	5.88
+Psychologie	Gesundheit	5.11
+Psychologie	Erkenntnis	4.92
+Planet	Konstellation	6.23
+Planet	Galaxie	7.08
+Planet	Weltraum	7.08
+Planet	Astronom	6.38
+Basis	Information	3.32
+Voraussetzung	Erkenntnis	3.15
+Präzedensfall	Gesetz	5.62
+Beispielhaft	Sammlung	1.92
+Vorbildlich	Gruppe	2.19
+Tasse	Kaffee	7.21
+Tasse	Gegenstand	6.08
+Tasse	Trinken	7.62
+Tasse	Essen	1.77
+Tasse	Substanz	1.69
+Tasse	Flüssigkeit	5.47
+Energie	Minister	4.15
+Minister	Senat	5.96
+Energie	Labor	3.23
+Computer	Labor	4.31
+Waffe	Geheimnis	1.85
+Polizei	Fingerabdruck	6.23
+Polizei	Ermittlung	7.27
+Untersuchung	Aufwand	4.15
+Mars	Wasser	2.77
+Mars	Wissenschaftler	5.54
+Nachrichten	Bericht	7.85
+Schlucht	Landschaft	6.54
+Bild	Oberfläche	3.38
+Entdeckung	Weltall	4.77
+Wasser	Leck	5.81
+Zeichen	Kerbe	3.92
+Mittwoch	Nachrichten	1.38
+Computer	Nachrichten	4.23
+Gebiet	Oberfläche	3.77
+Atmosphäre	Landschaft	2.50
+Präsident	Orden	2.77
+Krieg	Truppen	6.81
+Rekord	Nummer	2.77
+Theater	Geschichte	3.85
+Freiwilliger	Motto	0.77
+Vorurteil	Anerkennung	3.46
+Auszeichnung	Tapferkeit	5.92
+Jahrhundert	Nation	1.54
+Verzögerung	Rassismus	1.31
+Verzögerung	Nachrichten	1.08
+Minister	Partei	7.38
+Frieden	Plan	3.85
+Minderheit	Frieden	2.23
+Versuch	Frieden	3.46
+Regierung	Krise	5.65
+Aufmarsch	Abzug	6.27
+Aufmarsch	Rückzug	6.81
+Energie	Krise	4.77
+Ankündigung	Aufwand	1.08
+Schlaganfall	Krankenhaus	6.88
+Behinderung	Tod	2.42
+Opfer	Notfall	6.69
+Behandlung	Erholung	5.46
+Zeitschrift	Verein	1.77
+Arzt	Verantwortung	6.65
+Haftung	Versicherung	7.62
+Schule	Zentrum	4.00
+Ursache	Bluthochdruck	3.65
+Ursache	Kriterium	3.69
+Hundert	Prozent	6.92
+Tod	Trakt	2.46
+Tod	Insasse	2.38
+Rechtsanwalt	Beweis	6.04
+Leben	Dauer	6.42
+Wort	Ähnlichkeit	2.46
+Gremium	Empfehlung	3.65
+Direktor	Interview	2.46
+OPEC	Staat	3.92
+Frieden	Stimmung	4.46
+Frieden	Versicherung	2.15
+Gelände	Kilometer	3.46
+Wettbewerb	Preis	7.73
+Konsument	Vertrauen	3.85
+Konsument	Energie	3.69
+Problem	Flughafen	2.23
+Auto	Flug	3.77
+Kredit	Karte	6.31
+Vertrauen	Information	4.77
+Hotel	Reservierung	6.81
+Lebensmittel	Geld	4.88
+Registrierung	Abmachung	2.69
+Vereinbarung	Unterkunft	2.00
+Monat	Hotel	0.69
+Ankunft	Hotel	4.54
+Bett	Schrank	6.15
+Schrank	Kleider	7.50
+Lage	Schlussfolgerung	3.69
+Situation	Isolation	2.15
+Unparteilichkeit	Interesse	2.50
+Richtung	Verbindung	3.65
+Straße	Kinder	3.73
+Aufzählung	Nähe	0.92
+Liste	Kategorie	5.85
+Herstellung	Wanderung	0.69
+Richtwert	Kennziffer	4.92
+Medien	Handel	2.87
+Medien	Vorteil	1.81
+Gewinnanteil	Kalkulation	6.15
+Währung	Markt	6.03
+OPEC	Öl	7.65
+Öl	Aktie	5.14
+Ankündigung	Produktion	1.65
+Ankündigung	Warnung	6.06
+Gewinn	Warnung	2.85
+Dollar	Gewinn	5.25
+Dollar	Verlust	5.27
+Computer	Software	8.35
+Netzwerk	Hardware	6.88
+Zubehör	Hersteller	4.43
+Luxus	Auto	5.14
+Fünf	Monat	1.33
+Bericht	Zuwachs	2.02
+Investor	Einkommen	4.05
+Baseball	Saison	4.98
+Spiel	Sieg	7.08
+Spiel	Mannschaft	6.88
+Spiel	Serie	4.85
+Spiel	Niederlage	6.77
+Sieben	Reihe	2.41
+Meeresfrüchte	Meer	7.32
+Essen	Vorbereitung	5.58
+Video	Archiv	5.06
+Beginn	Jahr	4.70
+Beginn	Partie	4.22
+Spiel	Runde	6.33
+Boxen	Runde	7.35
+Kämpfen	Besiegen	7.41
+Grundsatz	Versicherung	2.51
+Tag	Sommer	3.33
+Sommer	Dürre	6.04
+Sommer	Natur	5.37
+Tag	Dämmerung	5.97
+Natur	Umwelt	8.00
+Umwelt	Nachhaltigkeit	6.54
+Natur	Mensch	6.31
+Seife	Oper	2.85
+Leben	Lektion	4.54
+Fokus	Leben	2.62
+Herstellung	Belegschaft	3.23
+Fernsehen	Film	7.31
+Liebhaber	Streit	4.08
+Zuschauer	Serie	5.38
+Möglichkeit	Mädchen	1.65
+Bevölkerung	Entwicklung	4.27
+Moral	Wichtigkeit	4.65
+Moral	Heirat	2.81
+Geschlecht	Gleichheit	3.81
+Änderung	Einstellung	4.15
+Familie	Planung	5.62
+Oper	Industrie	0.85
+Zucker	Annäherung	1.46
+Praxis	Institution	4.27
+Ministerium	Kultur	4.38
+Problem	Herausforderung	6.08
+Größe	Prominenz	5.46
+Staat	Bürger	6.77
+Planet	Menschen	5.62
+Entwicklung	Ausgabe	3.15
+Erfahrung	Musik	1.08
+Musik	Projekt	4.04
+Möglichkeit	Glaubwürdigkeit	2.69
+Ausstellungsstück	Erinnerungsstück	4.38
+Konzert	virtuos	4.46
+Betrachtung	Architektur	4.38
+Weltraum	Erde	6.46
+Erhaltung	Welt	4.31
+Einlass	Eintritt	8.08
+Regen	Flut	7.27
+Wetter	Vorhersage	6.46
+Katastrophe	Gebiet	4.27
+Präsident	Büro	3.42
+Architektur	Jahrhundert	3.08
\ No newline at end of file

WordSim353/data/multilingual_leviant_reichart/wordsim353-german-sim.txt

+Word1	Word2	Score
+Tiger	Katze	7.92
+Tiger	Tiger	10.00
+Flugzeug	Auto	4.92
+Zug	Auto	5.54
+Fernseher	Radio	5.77
+Medien	Radio	8.15
+Brot	Butter	5.62
+Gurke	Kartoffel	4.92
+Arzt	Krankenschwester	6.69
+Professor	Doktor	6.77
+Student	Professor	5.69
+klug	Student	4.85
+klug	dumm	5.00
+Vorrat	Telefon	0.31
+Vorrat	CD	0.54
+Vorrat	Jaguar	0.23
+Vorrat	Ei	2.15
+Aktie	Live	1.08
+Aktie	Leben	0.62
+Holz	Wald	8.54
+Geld	Bargeld	9.69
+Professor	Gurke	0.15
+König	Kohl	0.23
+König	Königin	10.00
+König	Turm	5.15
+Bischoff	Rabbi	7.00
+Heilig	Sex	0.69
+Ficken	Sex	9.15
+Fußball	Basketball	5.38
+Fußball	Tennis	4.69
+Arafat	Jackson	0.69
+Physik	Chemie	7.54
+Weltall	Chemie	3.31
+Wodka	Gin	7.92
+Wodka	Brandy	8.22
+Drink	Auto	1.85
+Trinken	Ohren	0.62
+Trinken	Essen	7.23
+Säugen	Mutter	7.69
+Auto	Fahrzeug	9.19
+Edelstein	Juwel	9.27
+Ausflug	Reise	8.23
+Junge	Bursche	9.27
+Irrenanstalt	Tollhaus	8.23
+Magier	Zauberer	9.65
+Ofen	Herd	8.81
+Essen	Frucht	6.77
+Vogel	Hahn	6.08
+Vogel	Kranich	7.46
+Kran	Arbeitsgerät	6.08
+Bursche	Bruder	4.23
+Mönch	Orakel	1.38
+Friedhof	Waldgebiet	1.92
+Essen	Hahn	3.46
+Küste	Hügel	2.69
+Wald	Friedhof	3.31
+Ufer	Waldgebiet	2.31
+Mönch	Sklave	1.08
+Küste	Wald	1.77
+Bursche	Zauberer	0.77
+Akkord	Lächeln	0.31
+Glas	Magier	1.69
+Mittag	Faden	0.15
+Hahn	Reise	0.31
+Geld	Dollar	7.92
+Geld	Bargeld	9.19
+Geld	Währung	8.12
+Geld	Wäsche	3.62
+Tiger	Jaguar	6.00
+Tiger	Katze	6.92
+Tiger	Raubtier	8.00
+Tiger	Säugetier	6.58
+Tiger	Tier	7.85
+Tiger	Organismus	3.59
+Tiger	Fauna	3.69
+Psychologie	Psychiatrie	6.85
+Psychologie	Wissenschaft	5.96
+Psychologie	Disziplin	4.77
+Planet	Stern	7.23
+Planet	Mond	7.08
+Planet	Sonne	7.08
+Präzedenz	Beispiel	6.83
+Basis	Information	3.32
+Voraussetzung	Erkenntnis	3.15
+Beispielhaft	Sammlung	1.92
+Vorbildlich	Gruppe	2.19
+Vorangehend	Vorausgehend	8.04
+Tasse	Geschirr	8.00
+Tasse	Gegenstand	6.08
+Tasse	Artefakt	2.00
+Tasse	Objekt	5.69
+Tasse	Ding	5.08
+Tasse	Essen	1.77
+Tasse	Substanz	1.69
+Jaguar	Katze	6.66
+Jaguar	Auto	7.55
+Energie	Minister	4.15
+Untersuchung	Aufwand	4.15
+Mars	Wasser	2.77
+Bild	Oberfläche	3.38
+Zeichen	Kerbe	3.92
+Mittwoch	Nachrichten	1.38
+Meile	Kilometer	7.62
+Computer	Nachrichten	4.23
+Atmosphäre	Landschaft	2.50
+Präsident	Orden	2.77
+Haut	Augen	4.12
+Japaner	Amerikaner	5.73
+Theater	Geschichte	3.85
+Freiwilliger	Motto	0.77
+Vorurteil	Anerkennung	3.46
+Jahrhundert	Jahr	6.85
+Jahrhundert	Nation	1.54
+Verzögerung	Rassismus	1.31
+Verzögerung	Nachrichten	1.08
+Frieden	Plan	3.85
+Minderheit	Frieden	2.23
+Versuch	Frieden	3.46
+Aufmarsch	Abzug	6.27
+Ankündigung	Nachrichten	6.31
+Ankündigung	Aufwand	1.08
+Zeitschrift	Verein	1.77
+Arzt	Personal	4.46
+Schule	Zentrum	4.00
+Ursache	Bluthochdruck	3.65
+Harvard	Yale	7.85
+Krankenhaus	Infrastruktur	5.00
+Leben	Tod	8.69
+Leben	Dauer	6.42
+Wort	Ähnlichkeit	2.46
+Gremium	Empfehlung	3.65
+Direktor	Interview	2.46
+Frieden	Stimmung	4.46
+Frieden	Versicherung	2.15
+Reise	Aktivität	6.31
+Konsument	Vertrauen	3.85
+Konsument	Energie	3.69
+Problem	Flughafen	2.23
+Auto	Flug	3.77
+Monat	Hotel	0.69
+Art	Sorte	8.81
+Lage	Schlussfolgerung	3.69
+Situation	Isolation	2.15
+Richtung	Verbindung	3.65
+Straße	Platz	5.35
+Straße	Allee	7.73
+Straße	Häuserblock	5.50
+Straße	Kinder	3.73
+Aufzählung	Nähe	0.92
+Zelle	Telefon	6.38
+Herstellung	Wanderung	0.69
+Richtwert	Kennziffer	4.92
+Medien	Handel	2.87
+Medien	Vorteil	1.81
+Gewinnanteil	Auszahlung	7.32
+Kalkulation	Berechnung	9.54
+Ankündigung	Produktion	1.65
+Gewinn	Warnung	2.85
+Gewinn	Verlust	7.85
+Dollar	Yen	7.31
+Dollar	Kohle	5.58
+Telefon	Zubehör	4.42
+Fünf	Monat	1.33
+Bericht	Zuwachs	2.02
+Flüssigkeit	Wasser	8.62
+Marathon	Sprint	7.67
+Sieben	Reihe	2.41
+Meeresfrüchte	Essen	7.23
+Meeresfrüchte	Hummer	6.81
+Hummer	Essen	6.78
+Hummer	Wein	4.15
+Beginn	Jahr	4.70
+Beginn	Partie	4.22
+Meisterschaft	Turnier	8.05
+Grundsatz	Versicherung	2.51
+Mann	Frau	8.69
+Mann	Präsident	5.46
+Mord	Totschlag	8.62
+Oper	Aufführung	6.38
+Fokus	Leben	2.62
+Zuschauer	Serie	5.38
+Möglichkeit	Mädchen	1.65
+Bevölkerung	Entwicklung	4.27
+Moral	Wichtigkeit	4.65
+Moral	Heirat	2.81
+Mexiko	Brasil	5.23
+Oper	Industrie	0.85
+Zucker	Annäherung	1.46
+Praxis	Institution	4.27
+Ministerium	Kultur	4.38
+Entwicklung	Ausgabe	3.15
+Erfahrung	Musik	1.08
+Musik	Projekt	4.04
+Glas	Metal	3.92
+Aluminium	Metal	7.81
+Möglichkeit	Glaubwürdigkeit	2.69
+Rock	Jazz	6.19
+Museum	Theater	5.42
+Betrachtung	Architektur	4.38
+Regen	Gewitter	7.85
+Architektur	Jahrhundert	3.08
\ No newline at end of file