解析文字提取关键词java

RAKE算法

RAKE算法用来做关键词(key word)的提取，实际上提取的是关键的短语(phrase)，并且倾向于较长的短语，在英文中，关键词通常包括多个单词，但很少包含标点符号和停用词，例如and，the，of等，以及其他不包含语义信息的单词。

算法思想

1. RAKE算法首先使用标点符号（如半角的句号、问号、感叹号、逗号等）将一篇文档分成若干分句。
2. 然后对于每一个分句，使用停用词作为分隔符将分句分为若干短语，这些短语作为最终提取出的关键词的候选词。
3. 最后，每个短语可以再通过空格分为若干个单词，可以通过给每个单词赋予一个得分，通过累加得到每个短语的得分。

一个关键点在于将这个短语中每个单词的共现关系考虑进去。最终定义的公式是:
$wordScore=\frac{wordDegree(w)}{wordFrequency(w)}$
词共现矩阵的两种不同含义：

1. 词共现矩阵的每一列的值即为该词的度deg（是一个网络中的概念，每与一个单词共现在一个短语中，度就加1，考虑该单词本身）,每个词在文本中出现的次数即为频率freq。
2. 通过统计一个事先指定大小的窗口内的word共现次数，以word周边的共现词的次数做为当前word的vector。具体来说，我们通过从大量的语料文本中构建一个共现矩阵来定义word representation。

对于第一解释存在以下示例：

共词矩阵对角线上元素赋值为它自身在所有文章出现次数。循环遍历特征词列表，构建全部两个词之间的组合,再遍历每一篇文章的切词结果，如果该两个词在同一片文章中出现，则该两词的权重+1，再将其存入共词矩阵的对应位置中。

实现可以参照[4]或[7]。

对于第二种解释有以下示例：

I like deep learning.
I like NLP.
I enjoy flying.

假设窗口大小为2，其词共现矩阵如下表所示：

表1 词共现矩阵

count	I	like	enjoy	deep	learning	NLP	flying	.
I	0	2	1	0	0	0	0	0
like	2	0	0	1	0	1	0	0
enjoy	1	0	0	0	0	0	1	0
deep	0	1	0	0	1	0	0	0
learning	0	0	0	1	0	0	0	1
NLP	0	1	0	0	0	0	0	1
flying	0	0	1	0	0	0	0	1
.	0	0	0	0	1	1	1	0

其中（I，like)=2是因为窗口等于3，“I like deep”和“I like NLP”中各出现了一次，其他的依此类推。

附页

代码实现

import re
import operator
import argparse
import codecs
 
def isNumber(s):
    try:
        float(s) if '.' in s else int(s)
        return True
    except ValueError:
        return False
 
class Rake:
    def __init__(self, inputFilePath, stopwordsFilePath, outputFilePath, minPhraseChar, maxPhraseLength):
        self.outputFilePath = outputFilePath
        self.minPhraseChar = minPhraseChar
        self.maxPhraseLength = maxPhraseLength
        # read documents
        self.docs = []
        for document in codecs.open(inputFilePath, 'r', 'utf-8'):
            self.docs.append(document)
        # read stopwords
        stopwords = []
        for word in codecs.open(stopwordsFilePath, 'r', 'utf-8'):
            stopwords.append(word.strip())
        stopwordsRegex = []
        for word in stopwords:
            regex = r'\b' + word + r'(?![\w-])'
            stopwordsRegex.append(regex)
        self.stopwordsPattern = re.compile('|'.join(stopwordsRegex), re.IGNORECASE)
 
    def separateWords(self, text):
        splitter = re.compile('[^a-zA-Z0-9_\\+\\-/]')
        words = []
        for word in splitter.split(text):
            word = word.strip().lower()
            # leave numbers in phrase, but don't count as words, since they tend to invalidate scores of their phrases
            if len(word) > 0 and word != '' and not isNumber(word):
                words.append(word)
        return words
    
    def calculatePhraseScore(self, phrases):
        # calculate wordFrequency and wordDegree
        wordFrequency = {}
        wordDegree = {}
        for phrase in phrases:
            wordList = self.separateWords(phrase)
            wordListLength = len(wordList)
            wordListDegree = wordListLength - 1
            for word in wordList:
                wordFrequency.setdefault(word, 0)
                wordFrequency[word] += 1
                wordDegree.setdefault(word, 0)
                wordDegree[word] += wordListDegree
        for item in wordFrequency:
            wordDegree[item] = wordDegree[item] + wordFrequency[item]
    
        # calculate wordScore = wordDegree(w)/wordFrequency(w)
        wordScore = {}
        for item in wordFrequency:
            wordScore.setdefault(item, 0)
            wordScore[item] = wordDegree[item] * 1.0 / wordFrequency[item]
 
        # calculate phraseScore
        phraseScore = {}
        for phrase in phrases:
            phraseScore.setdefault(phrase, 0)
            wordList = self.separateWords(phrase)
            candidateScore = 0
            for word in wordList:
                candidateScore += wordScore[word]
            phraseScore[phrase] = candidateScore
        return phraseScore
    
        
    def execute(self):
        file = codecs.open(self.outputFilePath,'w','utf-8')
        for document in self.docs:
            # split a document into sentences
            sentenceDelimiters = re.compile(u'[.!?,;:\t\\\\"\\(\\)\\\'\u2019\u2013]|\\s\\-\\s')
            sentences = sentenceDelimiters.split(document)
            # generate all valid phrases
            phrases = []
            for s in sentences:
                tmp = re.sub(self.stopwordsPattern, '|', s.strip())
                phrasesOfSentence = tmp.split("|")
                for phrase in phrasesOfSentence:
                    phrase = phrase.strip().lower()
                    if phrase != "" and len(phrase) >= self.minPhraseChar and len(phrase.split()) <= self.maxPhraseLength:
                        phrases.append(phrase)
    
            # calculate phrase score
            phraseScore = self.calculatePhraseScore(phrases)
            keywords = sorted(phraseScore.items(), key = operator.itemgetter(1), reverse=True)
            file.write(str(keywords[0:int(len(keywords)/3)]) + "\n")
        file.close()
        
def readParamsFromCmd():
    parser = argparse.ArgumentParser(description = "This is a python implementation of rake(rapid automatic keyword extraction).")
    parser.add_argument('inputFilePath', help = 'The file path of input document(s). One line represents a document.')
    parser.add_argument('stopwordsFilePath', help = 'The file path of stopwords, each line represents a word.')
    parser.add_argument('-o', '--outputFilePath', help = 'The file path of output. (default output.txt in current dir).', default = 'output.txt')
    parser.add_argument('-m', '--minPhraseChar', type = int, help = 'The minimum number of characters of a phrase.(default 1)', default = 1)
    parser.add_argument('-a', '--maxPhraseLength', type = int, help = 'The maximum length of a phrase.(default 3)', default = 3)
    return parser.parse_args()
 
params = readParamsFromCmd().__dict__
 
rake = Rake(params['inputFilePath'], params['stopwordsFilePath'], params['outputFilePath'], params['minPhraseChar'], params['maxPhraseLength'])
rake.execute()