95 lines
2.6 KiB
Python
95 lines
2.6 KiB
Python
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import nltk
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from nltk.chunk.regexp import *
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from nlp import tokenizer as tk
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KP_REGEX_1 = "<JJ|NN|NNP|NNS|NNPS>*<NN|NNP|NNS|NNPS|VB|VBG>"
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KP_REGEX_2 = "<JJ>?<NN|NNS>+<IN><NN|NNS>"
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KP_REGEX_3 = "<JJ|VBN>*<NN|NNS>"
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noun_phrase_grammar = r"""
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NBAR:
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{<NN.*|JJ>*<NN.*|VBG>} # Nouns and Adjectives, terminated with Nouns or -ing verbs
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KP:
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{<NBAR>}
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{<NBAR><IN><NBAR>} # Above, connected with in/of/etc...
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"""
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hulth_grammar = r"""
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NBAR:
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{<NN.*|JJ.*>*<NN.*|VBG>} # Nouns and Adjectives, terminated with Nouns or -ing verbs
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VBPART:
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{<VBG|VBP><NBAR>} # Verb in participle from, then nouns
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COUNT:
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{<CD><NBAR>} # Numbers then nouns
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NP:
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{<NBAR><IN><NBAR>}
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"""
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hulth_labels = ['NP','NBAR','COUNT','VBPART']
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def extract_candidates_from_set(set,tokenizer):
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"""
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Generates the candidate keyphrases for a document.
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:param set: the training, test or validation set
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:param tokenizer: which tokenizer to use
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:return: a dictionary where each document is associated with its candidate keyphrases
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"""
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candidates = {}
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for doc, str in set.items() :
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candidates[doc] = extract_candidates(str,tokenizer)
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return candidates
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def extract_candidates(document,tokenizer):
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"""
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Extracts the candidate keyphrases from a string.
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:param document: the string to analyze
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:param tokenizer: the tokenizer to use
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:return: the list of candidate keyphrases for the input document
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"""
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return extract_valid_tokens(tk.tokenize(document,tokenizer))
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def extract_valid_tokens(tokens):
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"""
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Given a list of tokens, returns the subsets of such list which are potential keyphrases according to
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the provided part-of-speech patterns.
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:param document: the token list to analyze
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:return: the list of candidate keyphrases for the input document
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"""
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postagged_doc = nltk.pos_tag(tokens)
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kp_rule_1 = ChunkRule(KP_REGEX_1,"")
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kp_rule_2 = ChunkRule(KP_REGEX_2, "")
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kp_rule_3 = ChunkRule(KP_REGEX_3, "")
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#chunk_parser = RegexpChunkParser([kp_rule_1, kp_rule_2, kp_rule_3],
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# chunk_label="KP")
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chunk_parser = RegexpParser(grammar=hulth_grammar)
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tree = chunk_parser.parse(postagged_doc)
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candidates = []
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for subtree in tree.subtrees():
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if subtree.label() in hulth_labels:
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candidate = []
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for leaf in subtree.leaves():
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candidate.append(leaf[0])
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if candidate not in candidates:
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candidates.append(candidate)
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return candidates
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