flashtext

flashtext

该模块可用于替换句子中的关键字或从句子中提取关键字。它基于FlashText算法。

安装

$ pip安装flashtext

API文档

可以在flashtext上找到文档。

用法

提取关键字

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> # keyword_processor.add_keyword(<unclean name>, <standardised name>)
>>> keyword_processor.add_keyword( ' Big Apple ' , ' New York ' )
>>> keyword_processor.add_keyword( ' Bay Area ' )
>>> keywords_found = keyword_processor.extract_keywords( ' I love Big Apple and Bay Area. ' )
>>> keywords_found
>>> # ['New York', 'Bay Area']

替换关键字

>>> keyword_processor.add_keyword( ' New Delhi ' , ' NCR region ' )
>>> new_sentence = keyword_processor.replace_keywords( ' I love Big Apple and new delhi. ' )
>>> new_sentence
>>> # 'I love New York and NCR region.'

案例敏感的例子

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor( case_sensitive = True )
>>> keyword_processor.add_keyword( ' Big Apple ' , ' New York ' )
>>> keyword_processor.add_keyword( ' Bay Area ' )
>>> keywords_found = keyword_processor.extract_keywords( ' I love big Apple and Bay Area. ' )
>>> keywords_found
>>> # ['Bay Area']

提取的关键字跨度

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword( ' Big Apple ' , ' New York ' )
>>> keyword_processor.add_keyword( ' Bay Area ' )
>>> keywords_found = keyword_processor.extract_keywords( ' I love big Apple and Bay Area. ' , span_info = True )
>>> keywords_found
>>> # [('New York', 7, 16), ('Bay Area', 21, 29)]

通过提取的关键字获取额外的信息

>>> from flashtext import KeywordProcessor
>>> kp = KeywordProcessor()
>>> kp.add_keyword( ' Taj Mahal ' , ( ' Monument ' , ' Taj Mahal ' ))
>>> kp.add_keyword( ' Delhi ' , ( ' Location ' , ' Delhi ' ))
>>> kp.extract_keywords( ' Taj Mahal is in Delhi. ' )
>>> # [('Monument', 'Taj Mahal'), ('Location', 'Delhi')]
>>> # NOTE : replace_keywords feature won't work with this.

没有关键字的干净名称

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword( ' Big Apple ' )
>>> keyword_processor.add_keyword( ' Bay Area ' )
>>> keywords_found = keyword_processor.extract_keywords( ' I love big Apple and Bay Area. ' )
>>> keywords_found
>>> # ['Big Apple', 'Bay Area']

同时添加多个关键字

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_dict = {
>>>     " java " : [ " java_2e " , " java programing " ],
>>>     " product management " : [ " PM " , " product manager " ]
>>> }
>>> # {'clean_name': ['list of unclean names']}
>>> keyword_processor.add_keywords_from_dict(keyword_dict)
>>> # Or add keywords from a list:
>>> keyword_processor.add_keywords_from_list([ " java " , " python " ])
>>> keyword_processor.extract_keywords( ' I am a product manager for a java_2e platform ' )
>>> # output ['product management', 'java']

删除关键字

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_dict = {
>>>     " java " : [ " java_2e " , " java programing " ],
>>>     " product management " : [ " PM " , " product manager " ]
>>> }
>>> keyword_processor.add_keywords_from_dict(keyword_dict)
>>> print (keyword_processor.extract_keywords( ' I am a product manager for a java_2e platform ' ))
>>> # output ['product management', 'java']
>>> keyword_processor.remove_keyword( ' java_2e ' )
>>> # you can also remove keywords from a list/ dictionary
>>> keyword_processor.remove_keywords_from_dict({ " product management " : [ " PM " ]})
>>> keyword_processor.remove_keywords_from_list([ " java programing " ])
>>> keyword_processor.extract_keywords( ' I am a product manager for a java_2e platform ' )
>>> # output ['product management']

在关键WordProcessor中检查术语数量

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_dict = {
>>>     " java " : [ " java_2e " , " java programing " ],
>>>     " product management " : [ " PM " , " product manager " ]
>>> }
>>> keyword_processor.add_keywords_from_dict(keyword_dict)
>>> print ( len (keyword_processor))
>>> # output 4

检查keywordprocessor中是否存在术语

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword( ' j2ee ' , ' Java ' )
>>> ' j2ee ' in keyword_processor
>>> # output: True
>>> keyword_processor.get_keyword( ' j2ee ' )
>>> # output: Java
>>> keyword_processor[ ' colour ' ] = ' color '
>>> keyword_processor[ ' colour ' ]
>>> # output: color

获取字典中的所有关键字

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword( ' j2ee ' , ' Java ' )
>>> keyword_processor.add_keyword( ' colour ' , ' color ' )
>>> keyword_processor.get_all_keywords()
>>> # output: {'colour': 'color', 'j2ee': 'Java'}

为了检测单词边界，当前除此 w [a-za-z0-9_]以外的任何字符都被视为单词边界。

设置或添加字符作为单词字符的一部分

>>> from flashtext import KeywordProcessor
>>> keyword_processor = KeywordProcessor()
>>> keyword_processor.add_keyword( ' Big Apple ' )
>>> print (keyword_processor.extract_keywords( ' I love Big Apple/Bay Area. ' ))
>>> # ['Big Apple']
>>> keyword_processor.add_non_word_boundary( ' / ' )
>>> print (keyword_processor.extract_keywords( ' I love Big Apple/Bay Area. ' ))
>>> # [] 

测试

$ git克隆https://github.com/vi3k6i5/flashtext
$ cd flashtext
$ pip安装pytest
$ python setup.py测试

构建文档

$ git克隆https://github.com/vi3k6i5/flashtext
$ cd flashtext/docs
$ pip安装狮身人面像
$制作html
$＃打开_build/html/index.html在浏览器中查看本地

为什么不言论？

这是一种基于Aho-Corasick算法和Trie词典的自定义算法。

FlashText花费的时间与REGEX相比找到术语。

FlashText与Regex相比，FlashText所花费的时间替换条款。

链接到代码进行基准测试查找功能并替换功能。

该库的想法来自以下stackoverflow问题。

引用

原始论文发表在FlashText算法上。

 @Article {2017arxiv171100046s，
   作者= {{singh}，V。}，
    title =“ {更换或检索文档中的关键字}”，
  日记= {arxiv e-prints}，
ArchivePrefix =“ arxiv”，
   eprint = {1711.00046}，
 primaryclass =“ cs.ds”，
 关键字= {计算机科学 - 数据结构和算法}，
     年= 2017年，
    月=十月，
   adsurl = {http://adsabs.harvard.edu/abs/2017arxiv171100046s}，
  adsnote = {由SAO/NASA天体物理数据系统提供
}

这篇文章发表在中等五自由状运动上。

贡献

• 问题跟踪器：https：//github.com/vi3k6i5/flashtext/issues
• 源代码：https：//github.com/vi3k6i5/flashtext/

执照

该项目是根据MIT许可证获得许可的。