如何在Python中使用WordNet获取单词域？

如何使用nltk Python模块和查找单词域

假设我有像（交易、即期汇票、支票、存折）这样的词，所有这些词的域都是“银行”。我们如何在Python中使用nltk和WordNet实现这一点

我正在尝试通过上下位关系：

例如：

from nltk.corpus import wordnet as wn
sports = wn.synset('sport.n.01')
sports.hyponyms()
[Synset('judo.n.01'), Synset('athletic_game.n.01'), Synset('spectator_sport.n.01'),    Synset('contact_sport.n.01'), Synset('cycling.n.01'), Synset('funambulism.n.01'), Synset('water_sport.n.01'), Synset('riding.n.01'), Synset('gymnastics.n.01'), Synset('sledding.n.01'), Synset('skating.n.01'), Synset('skiing.n.01'), Synset('outdoor_sport.n.01'), Synset('rowing.n.01'), Synset('track_and_field.n.01'), Synset('archery.n.01'), Synset('team_sport.n.01'), Synset('rock_climbing.n.01'), Synset('racing.n.01'), Synset('blood_sport.n.01')]

及

---

普林斯顿WordNet和NLTK的WN API中都没有明确的域信息

我建议您获取WordNet域资源的副本，然后使用域链接您的语法集，请参阅

注册并完成下载后，您将看到一个

wn-domains-3.2-20070223

textfile，它是一个以制表符分隔的文件，第一列是语音标识符的偏移部分，第二列包含由空格分隔的域标记，例如

00584282-v  military pedagogy
00584395-v  military school university
00584526-v  animals pedagogy
00584634-v  pedagogy
00584743-v  school university
00585097-v  school university
00585271-v  pedagogy
00585495-v  pedagogy
00585683-v  psychological_features

然后使用以下脚本访问Synset的域：

还可以在WordNet域资源中查找

wn affect

，它对于消除情感词的歧义非常有用

---

在更新的NLTK v3.0中，它附带了开放的多语言WordNet（），并且由于法语语法集共享相同的偏移量ID，因此您可以简单地将WND用作跨语言资源。法语引理名称可以这样访问：

# Gets domains given synset.
for ss in wn.all_synsets():
    ssid = str(ss.offset()).zfill(8) + "-" + ss.pos()
    if synset2domains[ssid]: # not all synsets are in WordNet Domain.
        print ss, ss.lemma_names('fre'), ssid, synset2domains[ssid]

---

请注意，NLTK的最新版本将synset属性更改为“get”函数：

synset.offset

->

synset.offset（）

正如@alvas所建议的，您可以使用WordNetDomains。您必须下载WordNet2.0（在其当前状态下，WordNetDomains不支持WordNet3.0的感知目录，这是NLTK使用的WordNet的默认版本）和WordNetDomains

• WordNet2.0可以从

• WordNetDomains可以从下载（获得许可后）

我创建了一个非常简单的应用程序，可以加载Python3.x中的两个资源，并提供一些可能需要的常用例程（例如获取一组链接到给定术语或给定语法集的域，等等）。WordNetDomains的数据加载来自@alvas

这就是它的样子（省略了大多数注释）：

---

我认为您也可以使用spacy库，请参见下面的代码：

代码取自spacy wordnet官方网站：

---

请展示一些研究的成果，到目前为止你尝试了什么？@Torxed Hello，我已经添加了我的试验。太好了，我还没有听说过这个数据库！顺便说一句，我似乎不是唯一一个困惑为什么有人会在自由许可下发布，然后不让下载变得容易的人：（因为自2007年以来没有发布过任何版本（？），最好也发布github项目的更新？）虽然人们很久没有在WSD中使用WND了，现在最热门的事情是大量无监督的东西，这些东西很有趣，但却无法获得更多的知识，比如WND=）这太棒了，@alvas！你知道法国的WordNet是否有WordNet域名吗？还有，你有网站吗？我很想看到你更多的作品…@duhaime，谢谢你的关注，但我不是WND的创建者或开发者，我只是另一个潜藏在水池周围的nlp研究员。我确实有一个网页，但我不会做非常令人兴奋的事情，为了避免url机器人，尝试谷歌搜索

alvations

。用OMW信息更新我的答案，我希望它能有所帮助。

from collections import defaultdict
from nltk.corpus import wordnet as wn

# Loading the Wordnet domains.
domain2synsets = defaultdict(list)
synset2domains = defaultdict(list)
for i in open('wn-domains-3.2-20070223', 'r'):
    ssid, doms = i.strip().split('\t')
    doms = doms.split()
    synset2domains[ssid] = doms
    for d in doms:
        domain2synsets[d].append(ssid)

# Gets domains given synset.
for ss in wn.all_synsets():
    ssid = str(ss.offset).zfill(8) + "-" + ss.pos()
    if synset2domains[ssid]: # not all synsets are in WordNet Domain.
        print ss, ssid, synset2domains[ssid]

# Gets synsets given domain.        
for dom in sorted(domain2synsets):
    print dom, domain2synsets[dom][:3]

# Gets domains given synset.
for ss in wn.all_synsets():
    ssid = str(ss.offset()).zfill(8) + "-" + ss.pos()
    if synset2domains[ssid]: # not all synsets are in WordNet Domain.
        print ss, ss.lemma_names('fre'), ssid, synset2domains[ssid]

from collections import defaultdict
from nltk.corpus import WordNetCorpusReader
from os.path import exists


class WordNetDomains:
    def __init__(self, wordnet_home):
        #This class assumes you have downloaded WordNet2.0 and WordNetDomains and that they are on the same data home.
        assert exists(f'{wordnet_home}/WordNet-2.0'), f'error: missing WordNet-2.0 in {wordnet_home}'
        assert exists(f'{wordnet_home}/wn-domains-3.2'), f'error: missing WordNetDomains in {wordnet_home}'

        # load WordNet2.0
        self.wn = WordNetCorpusReader(f'{wordnet_home}/WordNet-2.0/dict', 'WordNet-2.0/dict')

        # load WordNetDomains (based on https://stackoverflow.com/a/21904027/8759307)
        self.domain2synsets = defaultdict(list)
        self.synset2domains = defaultdict(list)
        for i in open(f'{wordnet_home}/wn-domains-3.2/wn-domains-3.2-20070223', 'r'):
            ssid, doms = i.strip().split('\t')
            doms = doms.split()
            self.synset2domains[ssid] = doms
            for d in doms:
                self.domain2synsets[d].append(ssid)

    def get_domains(self, word, pos=None):
        word_synsets = self.wn.synsets(word, pos=pos)
        domains = []
        for synset in word_synsets:
            domains.extend(self.get_domains_from_synset(synset))
        return set(domains)

    def get_domains_from_synset(self, synset):
        return self.synset2domains.get(self._askey_from_synset(synset), set())

    def get_synsets(self, domain):
        return [self._synset_from_key(key) for key in self.domain2synsets.get(domain, [])]

    def get_all_domains(self):
        return set(self.domain2synsets.keys())

    def _synset_from_key(self, key):
        offset, pos = key.split('-')
        return self.wn.synset_from_pos_and_offset(pos, int(offset))

    def _askey_from_synset(self, synset):
        return self._askey_from_offset_pos(synset.offset(), synset.pos())

    def _askey_from_offset_pos(self, offset, pos):
        return str(offset).zfill(8) + "-" + pos

import spacy

from spacy_wordnet.wordnet_annotator import WordnetAnnotator 

# Load an spacy model (supported models are "es" and "en")  nlp = spacy.load('en') nlp.add_pipe(WordnetAnnotator(nlp.lang), after='tagger') token = nlp('prices')[0]

# wordnet object link spacy token with nltk wordnet interface by giving acces to
# synsets and lemmas  token._.wordnet.synsets() token._.wordnet.lemmas()

# And automatically tags with wordnet domains token._.wordnet.wordnet_domains()

# Imagine we want to enrich the following sentence with synonyms sentence = nlp('I want to withdraw 5,000 euros')

# spaCy WordNet lets you find synonyms by domain of interest
# for example economy economy_domains = ['finance', 'banking'] enriched_sentence = []

# For each token in the sentence for token in sentence:
    # We get those synsets within the desired domains
    synsets = token._.wordnet.wordnet_synsets_for_domain(economy_domains)
    if synsets:
        lemmas_for_synset = []
        for s in synsets:
            # If we found a synset in the economy domains
            # we get the variants and add them to the enriched sentence
            lemmas_for_synset.extend(s.lemma_names())
            enriched_sentence.append('({})'.format('|'.join(set(lemmas_for_synset))))
    else:
        enriched_sentence.append(token.text)

# Let's see our enriched sentence print(' '.join(enriched_sentence))
# >> I (need|want|require) to (draw|withdraw|draw_off|take_out) 5,000 euros