From 4701ee43d7812f72c8292dbc2a9bfdf24663295d Mon Sep 17 00:00:00 2001
From: Nadia <nwarslan@cl.uni-heidelberg.de>
Date: Wed, 27 Feb 2019 23:15:13 +0100
Subject: [PATCH] added wsd_method 1
---
...hod1_nadia_confusion.py => wsd_method1.py} | 41 ++++++++++---------
1 file changed, 21 insertions(+), 20 deletions(-)
rename scripts/wsd/{wsd_method1_nadia_confusion.py => wsd_method1.py} (87%)
diff --git a/scripts/wsd/wsd_method1_nadia_confusion.py b/scripts/wsd/wsd_method1.py
similarity index 87%
rename from scripts/wsd/wsd_method1_nadia_confusion.py
rename to scripts/wsd/wsd_method1.py
index 5bfbcce..483f657 100644
--- a/scripts/wsd/wsd_method1_nadia_confusion.py
+++ b/scripts/wsd/wsd_method1.py
@@ -145,7 +145,8 @@ def get_distance(node_combi, dist_dict):
def map_distances(sentence, embed_dict):
"""
-
+ this function computes every distance between a word and all senses of other words
+ returns a dictionary {(id1,id2):distance,...}
"""
dist_dict ={}
for i in range(len(sentence[:-1])):
@@ -158,7 +159,7 @@ def map_distances(sentence, embed_dict):
def find_optimum(sentence, dist_dict):
"""
-
+ this function finds the optimal sense combination of words in a sentence
"""
optimum = [0,0]
for n_combi in node_combi(sentence):
@@ -171,13 +172,11 @@ def find_optimum(sentence, dist_dict):
return optimum
-def map_sense():
+def node_combi(sentence):
"""
-
+ IN:[int]
+ this function computes every possible sense combination of a given sentence
"""
-
-def node_combi(sentence):
-
for combi in itertools.product(*sentence):
yield combi
@@ -193,10 +192,11 @@ def write_answer_to_file(data, filename):
def iterate_over(senseval_data, label_embeddings, lemmata_mapping, id_mapping, sense_key_mapping):
"""
-
+ this function iterates over sentences in senseval data
+ computes the optimal combination of senses given a sentence
"""
ambig_sents = open_mapping(senseval_data)
-
+ # split sentence if it contains more than 6 words
for i, sent in enumerate(ambig_sents):
if len(sent) >=6:
ambig_sents[i] = sent[:6]
@@ -204,12 +204,16 @@ def iterate_over(senseval_data, label_embeddings, lemmata_mapping, id_mapping, s
sentences = [[[word['lemma'],word['pos'],word['key']] for word in sent] for sent in ambig_sents]
solutions = []
+
for sentence in sentences:
mapped_sent = map_words(sentence, lemmata_mapping)[0]
lemma_list = map_words(sentence, lemmata_mapping)[1]
+
+ # check if compound word were splitted in two
split = 0
if len(sentence) < len(mapped_sent):
split = len(mapped_sent) - len(sentence)
+ # saves the index of an unknown word and removes it from mapped_sent
no_key = []
for word in mapped_sent:
if word[0]=='U':
@@ -217,36 +221,33 @@ def iterate_over(senseval_data, label_embeddings, lemmata_mapping, id_mapping, s
mapped_sent = [word for word in mapped_sent if word[0]!='U']
+ # computes label embeddings for every node and saves them in a dict
embed_dict = embed(mapped_sent, label_embeddings)
-
+ # computes distances between nodes and saves them in to a dict
dist_dict = map_distances(mapped_sent, embed_dict)
-
+ # computes the optimal sense combination
optimum = find_optimum(mapped_sent, dist_dict)
-
+ # maps node id's to wn 3 synset id's
optimum = [id_mapping[str(o)] for o in optimum[1]]
- # map syn id to sense_key
+ # map synset id to sense_key
for i, o in enumerate(optimum):
key = tuple(o + [lemma_list[i]])
if key in sense_key_mapping.keys():
optimum[i] = sense_key_mapping[key]
else: optimum[i] = ''
-
+ # adds the removed unknown words
if len(no_key) != 0:
for el in no_key:
optimum.insert(el, '')
-
+ # resplits compound words
resplit = 0
-
for i, word in enumerate(sentence):
if split != 0:
if '-' in word[0]:
optimum[i] = optimum[i] + ' ' + optimum[i+1]
optimum.remove(optimum[i+1])
split -= 1
- #print(sentence)
- #print(mapped_sent)
- #print(optimum)
solution_sent = [(word[2],optimum[k]) for k, word in enumerate(sentence)]
solutions += solution_sent
@@ -262,7 +263,7 @@ if __name__ == '__main__':
sense_key_mapping = open_sense_keys(SENSE_KEY_MAPPING)
- solutions = iterate_over(SENSEVAL_2, label_embeddings, lemmata_mapping, sense_key_mapping)
+ solutions = iterate_over(SENSEVAL_2, label_embeddings, lemmata_mapping, id_mapping sense_key_mapping)
write_answer_to_file(solutions, OUTPUT2)
--
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