diff --git a/code/absinth_nx.py b/code/absinth_nx.py
index ebb30c95e6fe72b2bd8dc443092a7a896f0c3d02..02fd7ae2a74b79983b76fedb24c2e70cdc22abd5 100644
--- a/code/absinth_nx.py
+++ b/code/absinth_nx.py
@@ -10,7 +10,7 @@ import numpy as np # for calculations
nlp = spacy.load('en') # standard english nlp
-def frequencies(corpus_path, target, stop_words=[], allowed_tags=['NN','NNS','JJ','JJS','JJR','NNP','VBZ','VBG'], min_context_size = 4, max_nodes=100000, max_edges=10000000):
+def frequencies(corpus_path, target, stop_words=[], allowed_tags=['NN','NNS','JJ','JJS','JJR','NNP','VBZ','VBG'], min_context_size = 2, max_nodes=100000, max_edges=10000000):
node_freq = dict()
edge_freq = dict()
@@ -19,9 +19,9 @@ def frequencies(corpus_path, target, stop_words=[], allowed_tags=['NN','NNS','JJ
s_target = target.replace('_', ' ') #target word with spaces
i = 0
- for f in files[:]:
+ for f in files:
- if i % int(len(files[:])/23) == 0:
+ if i % int(len(files)/23) == 0:
file_ratio = i/len(files[:])
max_node_ratio = len(node_freq)/max_nodes
max_edge_ratio = len(edge_freq)/max_edges
@@ -166,54 +166,79 @@ def components(graph, hubs, target):
return T
-#Uses MST to disambiguate context, should ideally write to evaluator format
+def score(graph, from_node, to_node):
+
+ if nx.has_path(graph, from_node, to_node):
+
+ path = nx.shortest_path(graph, from_node, to_node, 'weight')
+ total_weight = 0
+
+ for i in range(1, len(path)):
+ sub_from, sub_to = path[i-1], path[i]
+ total_weight += graph[sub_from][sub_to]['weight']
+
+ return 1/(1+total_weight)
+
+ else:
+
+ return 0
+
+
def disambiguate(mst, hubs, contexts):
T = mst
H = hubs
C = [c.lower().strip() for c in contexts]
- backup_cluster = len(H)
- result = []
- for v in list(T.nodes):
+ score_dict = dict()
+ result = list()
+
+ for c in C:
- scores = []
+ idx = C.index(c) + 1
- for h in H:
+ #if no sense is found for a target word, we should assume that there only is one sense
+ if len(H) == 0:
- if nx.has_path(T,v,h):
-
- path = nx.shortest_path(T,v,h,'weight')
- total_weight = 0
+ result.append((0, idx))
+
+ else:
- for i in range(1, len(path)):
-
- total_weight += T[path[i-1]][path[i]]['weight']
+ doc = nlp(c)
+ texts = [tok.text for tok in doc]
+
+ scores = np.zeros(len(H)) #initialise with zeros for every sense
- scores.append(1/(1+total_weight))
+ for text in texts:
+ if text in T.nodes:
+
+ new_scores = list()
+
+ for h in H:
+ if (text, h) in score_dict:
+ new_scores.append(score_dict[(text,h)])
+ else:
+ new_score = score(T, text, h)
+ new_scores.append(new_score)
+ score_dict[(text,h)] = new_scores
+
+ scores = np.add(scores, new_scores)
+
+ else:
+
+ pass
+
+ #if the disambiguator could not detect a sense, it should return a singleton, ie. nothing
+ if np.max(scores) == 0:
+
+ pass
+
else:
- scores.append(0)
-
- T.nodes[v]['s'] = np.array([s if s == max(scores) else 0 for s in scores])
-
- for c in C:
-
- toks = [t.text for t in nlp(c)]
- vector = np.sum([T.nodes[t]['s'] if t in T.nodes else np.zeros(len(H)) for t in toks], axis=0)
-
- idx = C.index(c) + 1
-
- if len(vector) == 0: #if no senses are found -> all in one
- result.append((0, idx))
- elif max(vector) == 0: #if no sense matches -> singletons
- pass
- else:
- result.append((np.argmax(vector), idx))
-
- return result
+ result.append((np.argmax(scores), idx))
+ return result
def backup(contexts):
@@ -225,10 +250,10 @@ if __name__ == '__main__':
data_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/WSI-Evaluator/datasets/MORESQUE/'
#corpus_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/test'
- corpus_path = '/proj/absinth/wikipedia.txt.dump.20140615-en.SZTAKI/'
+ corpus_path = '/proj/absinth/wikipedia_reduced/'
results_path = '/home/students/zimmermann/Courses/ws17/fsem/absinth/clustering/'
- stop = set(stopwords.words('english') + ['utc', 'new', 'other', 'talk', 'wikipedia', 'article', 'topic', 'page', 'editors', 'encyclopedia', 'free'])
+ stop = set(stopwords.words('english') + ['utc', "'s", 'new', 'other', 'talk', 'wikipedia', 'article', 'topic', 'page', 'editors', 'encyclopedia', 'free'])
results = dict()
@@ -251,7 +276,7 @@ if __name__ == '__main__':
already_processed = [f.replace('.absinth', '') for f in os.listdir(results_path)]
- for line in topics_file.readlines()[1:]:
+ for line in topics_file.readlines()[1:5]:
l = line.split('\t')
if l[1] not in already_processed:
@@ -279,9 +304,9 @@ if __name__ == '__main__':
H = root_hubs(G, edge_freq)
for h in H:
mfn = sorted(G.adj[h], key=lambda key: edge_freq[h,key] if h < key else edge_freq[key, h], reverse=True)[:6]
- print(' {}: {}\n'.format(h, mfn))
+ print(' {}: {}'.format(h, mfn))
- print('[A] Building Minimum Spanning Tree.\n')
+ print('\n[A] Building Minimum Spanning Tree.\n')
T = components(G, H, target)
print('[A] Disambiguating Results...')
@@ -294,6 +319,3 @@ if __name__ == '__main__':
f.write(key+'.'+str(d[0])+'\t'+key+'.'+str(d[1])+'\n')
f.close()
-
- #nx.draw(T, with_labels=True)
- #plt.show()