如何使用nltk Python模块和WordNet找到单词域？
假设我有像（交易，需求汇票，支票，存折）这样的词，所有这些词的域名都是“BANK”。我们如何在Python中使用nltk和WordNet来获得这个？
我正在尝试通过上位词和下位词关系：
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')]
和
bark = wn.synset('bark.n.02')
bark.hypernyms()
[Synset('noise.n.01')]
答案 0 :(得分：11)
Princeton WordNet和NLTK的WN API中没有明确的域信息。
我建议您获取WordNet域资源的副本，然后使用域链接您的同义词集，请参阅http://wndomains.fbk.eu/
在您注册并完成下载后，您将看到一个wn-domains-3.2-20070223文本文件，这是一个制表符分隔的文件，第一列是offset-PartofSpeech标识符，第二列包含以空格分隔的域标记，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synsets的域：
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]
还要查找wn-affect，这对于消除WordNet域资源中的情感词语非常有用。
随着NLTK v3.0的更新，它带有Open Multilingual WordNet（http://compling.hss.ntu.edu.sg/omw/），并且由于法语同义词共享相同的偏移ID，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，最新版本的NLTK会将synset属性更改为“get”函数：Synset.offset - ＆gt; Synset.offset()
答案 1 :(得分：1)
正如@alvas建议的那样，您可以使用WordNetDomains。您必须同时下载WordNet2.0（WordNetDomains当前状态为WordNetDomains，不支持NLTK使用的WordNet默认版本WordNet3.0）和WordNetDomains。
我创建了一个非常简单的Python API，它可以同时加载Python3.x中的这两种资源，并提供您可能需要的一些常规例程（例如，将一组域链接到给定的术语或给定的同义词集，等等。）。 WordNetDomains的数据加载来自@alvas。
这是它的样子（省略了大多数注释）：
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
答案 2 :(得分：0)
我认为您也可以使用 spacy 库，请参阅以下代码：
代码取自 spacy-wordnet 官方网站 https://pypi.org/project/spacy-wordnet/：
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