def extractKeyphrases(text):
#tokenize the text using nltk
wordTokens = nltk.word_tokenize(text)
#assign POS tags to the words in the text
tagged = nltk.pos_tag(wordTokens)
textlist = [x[0] for x in tagged]
tagged = filter_for_tags(tagged)
tagged = normalize(tagged)
unique_word_set = unique_everseen([x[0] for x in tagged])
word_set_list = list(unique_word_set)
#this will be used to determine adjacent words in order to construct keyphrases with two words
graph = buildGraph(word_set_list)
#pageRank - initial value of 1.0, error tolerance of 0,0001,
calculated_page_rank = nx.pagerank(graph, weight='weight')
#most important words in ascending order of importance
keyphrases = sorted(calculated_page_rank, key=calculated_page_rank.get, reverse=True)
#the number of keyphrases returned will be relative to the size of the text (a third of the number of vertices)
aThird = len(word_set_list) / 3
keyphrases = keyphrases[0:aThird+1]
#take keyphrases with multiple words into consideration as done in the paper - if two words are adjacent in the text and are selected as keywords, join them
#together
modifiedKeyphrases = set([])
dealtWith = set([]) #keeps track of individual keywords that have been joined to form a keyphrase
i = 0
j = 1
while j < len(textlist):
firstWord = textlist[i]
secondWord = textlist[j]
if firstWord in keyphrases and secondWord in keyphrases:
keyphrase = firstWord + ' ' + secondWord
modifiedKeyphrases.add(keyphrase)
dealtWith.add(firstWord)
dealtWith.add(secondWord)
else:
if firstWord in keyphrases and firstWord not in dealtWith:
modifiedKeyphrases.add(firstWord)
#if this is the last word in the text, and it is a keyword,
#it definitely has no chance of being a keyphrase at this point
if j == len(textlist)-1 and secondWord in keyphrases and secondWord not in dealtWith:
modifiedKeyphrases.add(secondWord)
i = i + 1
j = j + 1
return modifiedKeyphrases
评论列表
文章目录
