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* Fix init_model to read supersenses from wordnet, not pre-computed supersenses file
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@ -28,7 +28,7 @@ from spacy.en.lemmatizer import Lemmatizer
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from spacy.vocab import Vocab
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from spacy.vocab import write_binary_vectors
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from spacy.parts_of_speech import NOUN, VERB, ADJ
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from spacy.parts_of_speech import NOUN, VERB, ADJ, ADV
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import spacy.senses
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@ -80,19 +80,14 @@ def _read_probs(loc):
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def _read_senses(loc):
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lexicon = defaultdict(lambda: defaultdict(list))
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sense_names = dict((s, i) for i, s in enumerate(spacy.senses.STRINGS))
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pos_ids = {'noun': NOUN, 'verb': VERB, 'adjective': ADJ}
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pos_tags = [None, NOUN, VERB, ADJ, ADV, None]
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for line in codecs.open(str(loc), 'r', 'utf8'):
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sense_strings = line.split()
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word = sense_strings.pop(0)
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for sense in sense_strings:
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pos, sense = sense[3:].split('.')
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if pos[0].upper() == 'A':
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continue
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sense_name = '%s_%s' % (pos[0].upper(), sense.lower())
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if sense_name != 'N_tops':
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sense_id = sense_names[sense_name]
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lexicon[word][pos_ids[pos]].append(sense_id)
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sense_key, synset_offset, sense_number, tag_cnt = line.split()
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lemma, lex_sense = sense_key.split('%')
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ss_type, lex_filenum, lex_id, head_word, head_id = lex_sense.split(':')
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pos = pos_tags[int(ss_type)]
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if pos is not None:
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lexicon[lemma][pos].append(int(lex_filenum))
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return lexicon
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@ -105,7 +100,7 @@ def setup_vocab(src_dir, dst_dir):
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write_binary_vectors(str(vectors_src), str(dst_dir / 'vec.bin'))
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vocab = Vocab(data_dir=None, get_lex_props=get_lex_props)
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clusters = _read_clusters(src_dir / 'clusters.txt')
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senses = _read_senses(src_dir / 'supersenses.txt')
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senses = _read_senses(src_dir / 'wordnet' / 'index.sense')
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probs = _read_probs(src_dir / 'words.sgt.prob')
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for word in set(clusters).union(set(senses)):
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if word not in probs:
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