#-*- encoding:utf-8 -*-
"""
@author: letian
@homepage: http://www.letiantian.me
@github: https://github.com/someus/
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import jieba.posseg as pseg
import codecs
import os,re
from . import util
def get_default_stop_words_file():
d = os.path.dirname(os.path.realpath(__file__))
return os.path.join(d, 'stopwords.txt')
class WordSegmentation(object):
""" 分词 """
def __init__(self, stop_words_file = None, allow_speech_tags = util.allow_speech_tags):
"""
Keyword arguments:
stop_words_file -- 保存停止词的文件路径，utf8编码，每行一个停止词。若不是str类型，则使用默认的停止词
allow_speech_tags -- 词性列表，用于过滤
"""
allow_speech_tags = [util.as_text(item) for item in allow_speech_tags]
self.default_speech_tag_filter = allow_speech_tags
self.stop_words = set()
self.stop_words_file = get_default_stop_words_file()
if type(stop_words_file) is str:
self.stop_words_file = stop_words_file
for word in codecs.open(self.stop_words_file, 'r', 'utf-8', 'ignore'):
self.stop_words.add(word.strip())
def segment(self, text, lower = True, use_stop_words = True, use_speech_tags_filter = False):
"""对一段文本进行分词，返回list类型的分词结果
Keyword arguments:
lower -- 是否将单词小写（针对英文）
use_stop_words -- 若为True，则利用停止词集合来过滤（去掉停止词）
use_speech_tags_filter -- 是否基于词性进行过滤。若为True，则使用self.default_speech_tag_filter过滤。否则，不过滤。
"""
text = util.as_text(text)
jieba_result = pseg.cut(text)
if use_speech_tags_filter == True:
jieba_result = [w for w in jieba_result if w.flag in self.default_speech_tag_filter]
else:
jieba_result = [w for w in jieba_result]
# 去除特殊符号
word_list = [w.word.strip() for w in jieba_result if w.flag!='x']
word_list = [word for word in word_list if len(word)>0]
if lower:
word_list = [word.lower() for word in word_list]
if use_stop_words:
word_list = [word.strip() for word in word_list if word.strip() not in self.stop_words]
return word_list
def segment_sentences(self, sentences, lower=True, use_stop_words=True, use_speech_tags_filter=False):
"""将列表sequences中的每个元素/句子转换为由单词构成的列表。
sequences -- 列表，每个元素是一个句子（字符串类型）
"""
res = []
for sentence in sentences:
res.append(self.segment(text=sentence,
lower=lower,
use_stop_words=use_stop_words,
use_speech_tags_filter=use_speech_tags_filter))
return res
class SentenceSegmentation(object):
""" 分句 """
def __init__(self, delimiters=util.sentence_delimiters):
"""
Keyword arguments:
delimiters -- 可迭代对象，用来拆分句子
"""
self.delimiters = set([util.as_text(item) for item in delimiters])
def segment(self, text):
res = re.sub(".”","”",util.as_text(text))
res = [re.sub("\n","\nou",res)]
util.debug(res)
util.debug(self.delimiters)
for sep in self.delimiters:
text, res = res, []
for seq in text:
res += seq.split(sep)
res = [s.strip() for s in res if len(s.strip()) > 0]
return res
class Segmentation(object):
def __init__(self, stop_words_file = None,
allow_speech_tags = util.allow_speech_tags,
delimiters = util.sentence_delimiters):
"""
Keyword arguments:
stop_words_file -- 停止词文件
delimiters -- 用来拆分句子的符号集合
"""
self.ws = WordSegmentation(stop_words_file=stop_words_file, allow_speech_tags=allow_speech_tags)
self.ss = SentenceSegmentation(delimiters=delimiters)
def segment(self, text, lower = False):
text = util.as_text(text)
sentences = self.ss.segment(text)
words_no_filter = self.ws.segment_sentences(sentences=sentences,
lower = lower,
use_stop_words = False,
use_speech_tags_filter = False)
words_no_stop_words = self.ws.segment_sentences(sentences=sentences,
lower = lower,
use_stop_words = True,
use_speech_tags_filter = False)
words_all_filters = self.ws.segment_sentences(sentences=sentences,
lower = lower,
use_stop_words = True,
use_speech_tags_filter = True)
return util.AttrDict(
sentences = sentences,
words_no_filter = words_no_filter,
words_no_stop_words = words_no_stop_words,
words_all_filters = words_all_filters
)
if __name__ == '__main__':
pass
