Question

我正在尝试做回指解决，下面就是我的代码。

首先我导航到我下载stanford模块的文件夹。然后我在命令提示符下运行命令来初始化stanford nlp模块

java -mx4g -cp "*;stanford-corenlp-full-2017-06-09/*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout 15000

之后我在Python中执行以下代码

from pycorenlp import StanfordCoreNLP
nlp = StanfordCoreNLP('http://localhost:9000')

我想将句子Tom is a smart boy. He know a lot of thing.更改为Tom is a smart boy. Tom know a lot of thing.，而Python中没有任何教程或任何帮助。

我能做的就是用Python下面的代码注释

共识决议

output = nlp.annotate(sentence, properties={'annotators':'dcoref','outputFormat':'json','ner.useSUTime':'false'})

并解析coref

coreferences = output['corefs']

我低于JSON

coreferences

{u'1': [{u'animacy': u'ANIMATE',
   u'endIndex': 2,
   u'gender': u'MALE',
   u'headIndex': 1,
   u'id': 1,
   u'isRepresentativeMention': True,
   u'number': u'SINGULAR',
   u'position': [1, 1],
   u'sentNum': 1,
   u'startIndex': 1,
   u'text': u'Tom',
   u'type': u'PROPER'},
  {u'animacy': u'ANIMATE',
   u'endIndex': 6,
   u'gender': u'MALE',
   u'headIndex': 5,
   u'id': 2,
   u'isRepresentativeMention': False,
   u'number': u'SINGULAR',
   u'position': [1, 2],
   u'sentNum': 1,
   u'startIndex': 3,
   u'text': u'a smart boy',
   u'type': u'NOMINAL'},
  {u'animacy': u'ANIMATE',
   u'endIndex': 2,
   u'gender': u'MALE',
   u'headIndex': 1,
   u'id': 3,
   u'isRepresentativeMention': False,
   u'number': u'SINGULAR',
   u'position': [2, 1],
   u'sentNum': 2,
   u'startIndex': 1,
   u'text': u'He',
   u'type': u'PRONOMINAL'}],
 u'4': [{u'animacy': u'INANIMATE',
   u'endIndex': 7,
   u'gender': u'NEUTRAL',
   u'headIndex': 4,
   u'id': 4,
   u'isRepresentativeMention': True,
   u'number': u'SINGULAR',
   u'position': [2, 2],
   u'sentNum': 2,
   u'startIndex': 3,
   u'text': u'a lot of thing',
   u'type': u'NOMINAL'}]}

对此有何帮助？

Answer 1

我有类似的问题。在尝试处理核心nlp之后，我使用神经coref解决了它。您可以使用以下代码通过神经Coref轻松完成这项工作：

导入空间

nlp = spacy.load（'en_coref_md'）

doc = nlp（u'电话区号仅在满足以下所有条件时才有效。不能为空。必须为数字。不能小于200。最小位数为3。 '）

打印（doc ._。coref_clusters）

打印（doc ._。coref_resolved）

以上代码的输出为： [电话区号：[电话区号，电话号码，电话号码]]

电话区号仅在满足以下所有条件时才有效。电话区号不能为空。电话区号应为数字。电话区号不能小于200。最小位数应为3。

为此，您将需要使用sp_acy，以及英语模型，例如en_coref_md或en_coref_lg或en_coref_sm。您可以参考以下链接以获得更好的解释：

https://github.com/huggingface/neuralcoref

Answer 2

这是使用CoreNLP输出的数据结构的一种可能的解决方案。提供所有信息。这并不是一个完整的解决方案，可能需要扩展才能处理所有情况，但这是一个很好的起点。

from pycorenlp import StanfordCoreNLP

nlp = StanfordCoreNLP('http://localhost:9000')


def resolve(corenlp_output):
    """ Transfer the word form of the antecedent to its associated pronominal anaphor(s) """
    for coref in corenlp_output['corefs']:
        mentions = corenlp_output['corefs'][coref]
        antecedent = mentions[0]  # the antecedent is the first mention in the coreference chain
        for j in range(1, len(mentions)):
            mention = mentions[j]
            if mention['type'] == 'PRONOMINAL':
                # get the attributes of the target mention in the corresponding sentence
                target_sentence = mention['sentNum']
                target_token = mention['startIndex'] - 1
                # transfer the antecedent's word form to the appropriate token in the sentence
                corenlp_output['sentences'][target_sentence - 1]['tokens'][target_token]['word'] = antecedent['text']


def print_resolved(corenlp_output):
    """ Print the "resolved" output """
    possessives = ['hers', 'his', 'their', 'theirs']
    for sentence in corenlp_output['sentences']:
        for token in sentence['tokens']:
            output_word = token['word']
            # check lemmas as well as tags for possessive pronouns in case of tagging errors
            if token['lemma'] in possessives or token['pos'] == 'PRP$':
                output_word += "'s"  # add the possessive morpheme
            output_word += token['after']
            print(output_word, end='')


text = "Tom and Jane are good friends. They are cool. He knows a lot of things and so does she. His car is red, but " \
       "hers is blue. It is older than hers. The big cat ate its dinner."

output = nlp.annotate(text, properties= {'annotators':'dcoref','outputFormat':'json','ner.useSUTime':'false'})

resolve(output)

print('Original:', text)
print('Resolved: ', end='')
print_resolved(output)

这将提供以下输出：

Original: Tom and Jane are good friends. They are cool. He knows a lot of things and so does she. His car is red, but hers is blue. It is older than hers. The big cat ate his dinner.
Resolved: Tom and Jane are good friends. Tom and Jane are cool. Tom knows a lot of things and so does Jane. Tom's car is red, but Jane's is blue. His car is older than Jane's. The big cat ate The big cat's dinner.

如您所见，当代词具有句子首字母（标题大小写）的先行词（在最后一句中用“大猫”代替“大猫”）时，此解决方案不涉及更正情况。这取决于先行词的类别-普通名词先词需要小写，而专有名词先词则不需要。其他一些临时处理可能是必要的（关于我测试语句中的所有格）。它还假定您不希望重复使用原始输出令牌，因为它们已被此代码修改。一种解决方法是复制原始数据结构或创建新属性，并相应地更改print_resolved函数。纠正任何分辨率错误也是另一个挑战！

Answer 3

from stanfordnlp.server import CoreNLPClient
from nltk import tokenize

client = CoreNLPClient(annotators=['tokenize','ssplit', 'pos', 'lemma', 'ner', 'parse', 'coref'], memory='4G', endpoint='http://localhost:9001')

def pronoun_resolution(text):

    ann = client.annotate(text)
    modified_text = tokenize.sent_tokenize(text)

    for coref in ann.corefChain:

        antecedent = []
        for mention in coref.mention:
            phrase = []
            for i in range(mention.beginIndex, mention.endIndex):
                phrase.append(ann.sentence[mention.sentenceIndex].token[i].word)
            if antecedent == []:
                antecedent = ' '.join(word for word in phrase)
            else:
                anaphor = ' '.join(word for word in phrase)
                modified_text[mention.sentenceIndex] = modified_text[mention.sentenceIndex].replace(anaphor, antecedent)

    modified_text = ' '.join(modified_text)

    return modified_text

text = 'Tom is a smart boy. He knows a lot of things.'
pronoun_resolution(text)

输出：“汤姆是个聪明的男孩。汤姆很了解。”

使用python的stanford-nlp中的回指分辨率

3 个答案: