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为函数textrank增加参数allowPOS,并修改extract_tags的参数allowPOS与textrank保持一致。
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@ -59,14 +59,14 @@ def set_stop_words(stop_words_path):
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for line in lines:
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for line in lines:
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STOP_WORDS.add(line)
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STOP_WORDS.add(line)
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def extract_tags(sentence, topK=20, withWeight=False, allowPOS=[]):
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def extract_tags(sentence, topK=20, withWeight=False, allowPOS=['ns', 'n', 'vn', 'v']):
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"""
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"""
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Extract keywords from sentence using TF-IDF algorithm.
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Extract keywords from sentence using TF-IDF algorithm.
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Parameter:
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Parameter:
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- topK: return how many top keywords. `None` for all possible words.
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- topK: return how many top keywords. `None` for all possible words.
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- withWeight: if True, return a list of (word, weight);
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- withWeight: if True, return a list of (word, weight);
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if False, return a list of words.
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if False, return a list of words.
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- allowPOS: the allowed POS list eg. ['n'].
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- allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v'].
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if the POS of w is not in this list,it will be filtered.
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if the POS of w is not in this list,it will be filtered.
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"""
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"""
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global STOP_WORDS, idf_loader
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global STOP_WORDS, idf_loader
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@ -48,15 +48,17 @@ class UndirectWeightedGraph:
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return ws
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return ws
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def textrank(sentence, topK=10, withWeight=False):
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def textrank(sentence, topK=10, withWeight=False, allowPOS=['ns', 'n', 'vn', 'v']):
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"""
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"""
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Extract keywords from sentence using TextRank algorithm.
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Extract keywords from sentence using TextRank algorithm.
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Parameter:
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Parameter:
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- topK: return how many top keywords. `None` for all possible words.
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- topK: return how many top keywords. `None` for all possible words.
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- withWeight: if True, return a list of (word, weight);
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- withWeight: if True, return a list of (word, weight);
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if False, return a list of words.
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if False, return a list of words.
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- allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v'].
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if the POS of w is not in this list,it will be filtered.
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"""
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"""
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pos_filt = frozenset(('ns', 'n', 'vn', 'v'))
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pos_filt = frozenset(allowPOS)
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g = UndirectWeightedGraph()
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g = UndirectWeightedGraph()
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cm = collections.defaultdict(int)
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cm = collections.defaultdict(int)
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span = 5
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span = 5
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