python – spacy lemmatizer如何工作？

2022-07-16 10:25:28

对于词形还原,spacy有一个lists of words：形容词,副词,动词……还有例外列表：adverbs_irreg …对于常规词,有一组rules

我们以“更广泛”这个词为例

因为它是一个形容词,所以词典化的规则应该从这个列表中取出：

ADJECTIVE_RULES = [
    ["er", ""],
    ["est", ""],
    ["er", "e"],
    ["est", "e"]
]

据我所知,这个过程将是这样的：

1)获取单词的POS标签,以了解它是名词,动词……
2)如果没有应用其中一个规则,如果单词在不规则案例列表中被直接替换.

现在,如何决定使用“呃” – > “e”而不是“er” – > “”得到“宽”而不是“wid”？

Here它可以测试.

解决方法:

让我们从类定义开始：https://github.com/explosion/spaCy/blob/develop/spacy/lemmatizer.py

类

它从初始化3个变量开始：

class Lemmatizer(object):
    @classmethod
    def load(cls, path, index=None, exc=None, rules=None):
        return cls(index or {}, exc or {}, rules or {})

    def __init__(self, index, exceptions, rules):
        self.index = index
        self.exc = exceptions
        self.rules = rules

现在,看看self.exc的英文,我们看到它指向https://github.com/explosion/spaCy/tree/develop/spacy/lang/en/lemmatizer/init.py,它从目录https://github.com/explosion/spaCy/tree/master/spacy/en/lemmatizer加载文件

为什么Spacy不读文件？

很可能是因为声明字符串in-code比通过I / O流式传输字符串更快.

这些索引,例外和规则来自哪里？

仔细观察,它们似乎都来自最初的普林斯顿WordNet https://wordnet.princeton.edu/man/wndb.5WN.html

规则

再看一下,https://github.com/explosion/spaCy/tree/develop/spacy/lang/en/lemmatizer/_lemma_rules.py的规则类似于nltk https://github.com/nltk/nltk/blob/develop/nltk/corpus/reader/wordnet.py#L1749中的_morphy规则

这些规则最初来自Morphy软件https://wordnet.princeton.edu/man/morphy.7WN.html

另外,spacy包含了一些不是来自Princeton Morphy的标点规则：

PUNCT_RULES = [
    ["“", "\""],
    ["”", "\""],
    ["\u2018", "'"],
    ["\u2019", "'"]
]

例外

至于异常,它们存储在spacy中的* _irreg.py文件中,看起来它们也来自普林斯顿Wordnet.

很明显,如果我们查看原始WordNet .exc(排除)文件的某个镜像(例如https://github.com/extjwnl/extjwnl-data-wn21/blob/master/src/main/resources/net/sf/extjwnl/data/wordnet/wn21/adj.exc),如果从nltk下载wordnet包,我们会看到它是相同的列表：

alvas@ubi:~/nltk_data/corpora/wordnet$ls
adj.exc       cntlist.rev  data.noun  index.adv    index.verb  noun.exc
adv.exc       data.adj     data.verb  index.noun   lexnames    README
citation.bib  data.adv     index.adj  index.sense  LICENSE     verb.exc
alvas@ubi:~/nltk_data/corpora/wordnet$wc -l adj.exc 
1490 adj.exc

指数

如果我们看一下spacy lemmatizer的索引,我们发现它也来自Wordnet,例如： https://github.com/explosion/spaCy/tree/develop/spacy/lang/en/lemmatizer/_adjectives.py和nltk中重新分发的wordnet副本：

alvas@ubi:~/nltk_data/corpora/wordnet$head -n40 data.adj 

  1 This software and database is being provided to you, the LICENSEE, by  
  2 Princeton University under the following license.  By obtaining, using  
  3 and/or copying this software and database, you agree that you have  
  4 read, understood, and will comply with these terms and conditions.:  
  5   
  6 Permission to use, copy, modify and distribute this software and  
  7 database and its documentation for any purpose and without fee or  
  8 royalty is hereby granted, provided that you agree to comply with  
  9 the following copyright notice and statements, including the disclaimer,  
  10 and that the same appear on ALL copies of the software, database and  
  11 documentation, including modifications that you make for internal  
  12 use or for distribution.  
  13   
  14 WordNet 3.0 Copyright 2006 by Princeton University.  All rights reserved.  
  15   
  16 THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON  
  17 UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR  
  18 IMPLIED.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON  
  19 UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT-  
  20 ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE  
  21 OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT  
  22 INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR  
  23 OTHER RIGHTS.  
  24   
  25 The name of Princeton University or Princeton may not be used in  
  26 advertising or publicity pertaining to distribution of the software  
  27 and/or database.  Title to copyright in this software, database and  
  28 any associated documentation shall at all times remain with  
  29 Princeton University and LICENSEE agrees to preserve same.  
00001740 00 a 01 able 0 005 = 05200169 n 0000 = 05616246 n 0000 + 05616246 n 0101 + 05200169 n 0101 ! 00002098 a 0101 | (usually followed by `to') having the necessary means or skill or know-how or authority to do something; "able to swim"; "she was able to program her computer"; "we were at last able to buy a car"; "able to get a grant for the project"  
00002098 00 a 01 unable 0 002 = 05200169 n 0000 ! 00001740 a 0101 | (usually followed by `to') not having the necessary means or skill or know-how; "unable to get to town without a car"; "unable to obtain funds"  
00002312 00 a 02 abaxial 0 dorsal 4 002 ;c 06037666 n 0000 ! 00002527 a 0101 | facing away from the axis of an organ or organism; "the abaxial surface of a leaf is the underside or side facing away from the stem"  
00002527 00 a 02 adaxial 0 ventral 4 002 ;c 06037666 n 0000 ! 00002312 a 0101 | nearest to or facing toward the axis of an organ or organism; "the upper side of a leaf is known as the adaxial surface"  
00002730 00 a 01 acroscopic 0 002 ;c 06066555 n 0000 ! 00002843 a 0101 | facing or on the side toward the apex  
00002843 00 a 01 basiscopic 0 002 ;c 06066555 n 0000 ! 00002730 a 0101 | facing or on the side toward the base  
00002956 00 a 02 abducent 0 abducting 0 002 ;c 06080522 n 0000 ! 00003131 a 0101 | especially of muscles; drawing away from the midline of the body or from an adjacent part  
00003131 00 a 03 adducent 0 adductive 0 adducting 0 003 ;c 06080522 n 0000 + 01449236 v 0201 ! 00002956 a 0101 | especially of muscles; bringing together or drawing toward the midline of the body or toward an adjacent part  
00003356 00 a 01 nascent 0 005 + 07320302 n 0103 ! 00003939 a 0101 & 00003553 a 0000 & 00003700 a 0000 & 00003829 a 0000 |  being born or beginning; "the nascent chicks"; "a nascent insurgency"   
00003553 00 s 02 emergent 0 emerging 0 003 & 00003356 a 0000 + 02625016 v 0102 + 00050693 n 0101 | coming into existence; "an emergent republic"  
00003700 00 s 01 dissilient 0 002 & 00003356 a 0000 + 07434782 n 0101 | bursting open with force, as do some ripe seed vessels

基于spacy引理器使用的字典,异常和规则主要来自普林斯顿WordNet及其Morphy软件,我们可以继续看看spacy如何使用索引和异常来应用规则的实际实现.

我们回到https://github.com/explosion/spaCy/blob/develop/spacy/lemmatizer.py

主要动作来自函数而不是Lemmatizer类：

def lemmatize(string, index, exceptions, rules):
    string = string.lower()
    forms = []
    # TODO: Is this correct? See discussion in Issue #435.
    #if string in index:
    #    forms.append(string)
    forms.extend(exceptions.get(string, []))
    oov_forms = []
    for old, new in rules:
        if string.endswith(old):
            form = string[:len(string) - len(old)] + new
            if not form:
                pass
            elif form in index or not form.isalpha():
                forms.append(form)
            else:
                oov_forms.append(form)
    if not forms:
        forms.extend(oov_forms)
    if not forms:
        forms.append(string)
    return set(forms)

为什么Lemmatizer类之外的lemmatize方法？

我不完全确定,但也许,确保可以在类实例之外调用词形还原函数,但是假设存在@staticmethod and @classmethod,可能还有其他考虑因素为什么函数和类已被解耦

Morphy vs Spacy

比较spacy lemmatize()函数与nltk中的morphy()函数(最初来自于十多年前创建的http://blog.osteele.com/2004/04/pywordnet-20/),morphy(),Oliver Steele的WordNet形态的Python端口中的主要过程是：

>检查例外列表
>将一次规则应用于输入以获得y1,y2,y3等.
>返回数据库中的所有内容(并检查原始数据库)
>如果没有匹配项,请继续应用规则,直到找到匹配项
>如果找不到任何内容,请返回一个空列表

对于spacy,可能,它仍在开发中,鉴于TODO在https://github.com/explosion/spaCy/blob/develop/spacy/lemmatizer.py#L76行

但一般过程似乎是：

>查找异常,如果异常列表中的引理(如果单词在其中),则获取它们.
>应用规则
>保存索引列表中的那些
>如果步骤1-3中没有引理,那么只需跟踪词汇外单词(OOV),并将原始字符串附加到引理表单
>返回引理表格

在OOV处理方面,如果没有找到词形化形式,spacy返回原始字符串,在这方面,morphy的nltk实现也是如此,例如.

>>> from nltk.stem import WordNetLemmatizer
>>> wnl = WordNetLemmatizer()
>>> wnl.lemmatize('alvations')
'alvations'

在词形还原之前检查不定式

可能另一个不同点是morphy和spacy如何决定分配给单词的POS.在这方面,spacy puts some linguistics rule in the Lemmatizer() to decide whether a word is the base form and skips the lemmatization entirely if the word is already in the infinitive form (is_base_form()),如果要对语料库中的所有单词进行词形还原,这将节省相当多的时间,并且其中很大一部分是不定式(已经是引理形式).

但这在spacy中是可能的,因为它允许lemmatizer访问与某些形态规则紧密相关的POS.虽然对于morphy虽然可以使用细粒度的PTB POS标签找出一些形态,但仍然需要花费一些精力来对它们进行排序以了解哪些形式是不定式的.

一般来说,形态特征的3个主要信号需要在POS标签中取消：

>人
>数量
>性别

更新

SpaCy在最初的答案(5月12日)之后确实对他们的lemmatizer进行了更改.我认为目的是在没有查找和规则处理的情况下更快地进行词形还原.

因此,他们将词语预先词形化,并将它们保留在查找哈希表中,以便为他们预先词形化的词语检索O(1)https://github.com/explosion/spaCy/blob/master/spacy/lang/en/lemmatizer/lookup.py

此外,为了统一跨语言的词形变换器,该词形变换器现在位于https://github.com/explosion/spaCy/blob/develop/spacy/lemmatizer.py#L92

但上面讨论的底层词形还原步骤仍然与当前的spacy版本相关(4d2d7d586608ddc0bcb2857fb3c2d0d4c151ebfc)

结语

我想现在我们知道它适用于语言学规则和所有,另一个问题是“是否存在任何非基于规则的词形还原方法？”

但在回答之前的问题之前,“究竟什么是引理？”可能是更好的问题.

上一篇：python中的Spacy语言模型安装从_mklinit返回ImportError(ImportError：DLL加载失败：找不到指定的模块.)


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