jieba source parsing (B): jieba.cut

The previous chapter described the building before jieba word about the prefix dictionary , chapter introduces the main jieba: jieba.cut .
jieba word has three modes: Full mode, fine mode, search mode. Full mode and precise mode jieba.cut achieve search engine mode corresponds cut_for_search, and a three-caught HMM parameters can be determined by whether the new word recognition. Official Examples:

# encoding=utf-8
import jieba

seg_list = jieba.cut("我来到北京清华大学", cut_all=True)
print("Full Mode: " + "/ ".join(seg_list))  # 全模式
# 【全模式】: 我/ 来到/ 北京/ 清华/ 清华大学/ 华大/ 大学

seg_list = jieba.cut("我来到北京清华大学", cut_all=False)
print("Default Mode: " + "/ ".join(seg_list))  # 精确模式
# 【精确模式】: 我/ 来到/ 北京/ 清华大学

seg_list = jieba.cut("他来到了网易杭研大厦")  # 默认是精确模式
print(", ".join(seg_list))
# 【新词识别】：他, 来到, 了, 网易, 杭研, 大厦    (此处，“杭研”并没有在词典中，但是也被Viterbi算法识别出来了)

seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造")  # 搜索引擎模式
print(", ".join(seg_list))
# 【搜索引擎模式】： 小明, 硕士, 毕业, 于, 中国, 科学, 学院, 科学院, 中国科学院, 计算, 计算所, 后, 在, 日本, 京都, 大学, 日本京都大学, 深造

jieba.cut

Word main function

def cut(self, sentence, cut_all=False, HMM=True):
        '''
        jieba分词主函数，返回generator
        参数:
            - sentence: 待切分文本.
            - cut_all: 切分模式. True 全模式, False 精确模式.
            - HMM: 是否使用隐式马尔科夫.
        '''
        sentence = strdecode(sentence)  # sentence转unicode

        if cut_all:
            # re_han_cut_all = re.compile("([\u4E00-\u9FD5]+)", re.U)   
            re_han = re_han_cut_all  
            # re_skip_cut_all = re.compile("[^a-zA-Z0-9+#\n]", re.U)  
            re_skip = re_skip_cut_all    
        else:
            # re_han_default = re.compile("([\u4E00-\u9FD5a-zA-Z0-9+#&\._%]+)", re.U)
            re_han = re_han_default                
            # re_skip_default = re.compile("(\r\n|\s)", re.U)
            re_skip = re_skip_default   

        if cut_all:   
            cut_block = self.__cut_all   # cut_all=True, HMM=True or False
        elif HMM:
            cut_block = self.__cut_DAG   # cut_all=False, HMM=True
        else:
            cut_block = self.__cut_DAG_NO_HMM   # cut_all=False, HMM=False
        blocks = re_han.split(sentence)
        for blk in blocks:
            if not blk:
                continue
            if re_han.match(blk):    #  符合re_han匹配的串
                for word in cut_block(blk):
                    yield word
            else:
                tmp = re_skip.split(blk)
                for x in tmp:
                    if re_skip.match(x):
                        yield x
                    elif not cut_all:
                        for xx in x:
                            yield xx
                    else:
                        yield x

Each word can be seen that an iterative returns jieba.cut generator, may be used for loop obtained after word obtained ( can be returned directly by jieba.lcut result word list ).

• cut_all = True, HMM = _ corresponding to the full mode, i.e. all occurrences of words in the dictionary will be sliced ​​out, implemented function __cut_all;
• cut_all = False, HMM = False corresponding to the HMM without using the fine mode; press Unigram gram joint probability to find the maximum word combinations, to achieve function __cut_DAG;
• cut_all = False, HMM = True corresponding to the precise mode and using the HMM; maximum joint probability based on a combination of the word, the HMM identify unknown words, function implemented __cut_DAG_NO_HMM.

def __cut_DAG_NO_HMM(self, sentence):
    DAG = self.get_DAG(sentence)   # 构建有向无环图
    route = {}
    self.calc(sentence, DAG, route)   # 动态规划计算最短路
    x = 0
    N = len(sentence)
    buf = ''
    while x < N:
        y = route[x][1] + 1
        l_word = sentence[x:y]
        if re_eng.match(l_word) and len(l_word) == 1:
            buf += l_word
            x = y
        else:
            if buf:
                yield buf
                buf = ''
            yield l_word
            x = y
    if buf:
        yield buf
        buf = ''