理解和使用共指解析斯坦福 NLP 工具（在 Python 3.7 中）

Question

我正在尝试了解 Coreference NLP Stanford 工具。 这是我的代码，它正在运行：

import os
os.environ["CORENLP_HOME"] = "/home/daniel/StanfordCoreNLP/stanford-corenlp-4.0.0"

from stanza.server import CoreNLPClient

text = 'When he came from Brazil, Daniel was fortified with letters from Conan but otherwise did not know a soul except Herbert. Yet this giant man from the Northeast, who had never worn an overcoat or experienced a change of seasons, did not seem surprised by his past.'

with CoreNLPClient(annotators=['tokenize','ssplit','pos','lemma','ner', 'parse', 'depparse','coref'],
               properties={'annotators': 'coref', 'coref.algorithm' : 'neural'},timeout=30000, memory='16G') as client:

    ann = client.annotate(text)

chains = ann.corefChain
chain_dict=dict()
for index_chain,chain in enumerate(chains):
    chain_dict[index_chain]={}
    chain_dict[index_chain]['ref']=''
    chain_dict[index_chain]['mentions']=[{'mentionID':mention.mentionID,
                                          'mentionType':mention.mentionType,
                                          'number':mention.number,
                                          'gender':mention.gender,
                                          'animacy':mention.animacy,
                                          'beginIndex':mention.beginIndex,
                                          'endIndex':mention.endIndex,
                                          'headIndex':mention.headIndex,
                                          'sentenceIndex':mention.sentenceIndex,
                                          'position':mention.position,
                                          'ref':'',
                                          } for mention in chain.mention ]


for k,v in chain_dict.items():
    print('key',k)
    mentions=v['mentions']
    for mention in mentions:
        words_list = ann.sentence[mention['sentenceIndex']].token[mention['beginIndex']:mention['endIndex']]
        mention['ref']=' '.join(t.word for t in words_list)
        print(mention['ref'])
    

我尝试了三种算法：

1. 统计（如上面的代码）。 结果：

he
this giant man from the Northeast , who had never worn an overcoat or experienced a change of seasons
Daniel
his

1. 神经

this giant man from the Northeast , who had never worn an overcoat or experienced a change of seasons ,
his

1. 确定性（我收到以下错误）

    > Starting server with command: java -Xmx16G -cp
    > /home/daniel/StanfordCoreNLP/stanford-corenlp-4.0.0/*
    > edu.stanford.nlp.pipeline.StanfordCoreNLPServer -port 9000 -timeout
    > 30000 -threads 5 -maxCharLength 100000 -quiet True -serverProperties
    > corenlp_server-9fedd1e9dfb14c9e.props -preload
    > tokenize,ssplit,pos,lemma,ner,parse,depparse,coref Traceback (most
    > recent call last):
    > 
    >   File "<ipython-input-58-0f665f07fd4d>", line 1, in <module>
    >     runfile('/home/daniel/Documentos/Working Papers/Leader traits/Code/20200704 - Modeling
    > Organizing/understanding_coreference.py',
    > wdir='/home/daniel/Documentos/Working Papers/Leader
    > traits/Code/20200704 - Modeling Organizing')
    > 
    >   File
    > "/home/daniel/anaconda3/lib/python3.7/site-packages/spyder_kernels/customize/spydercustomize.py",
    > line 827, in runfile
    >     execfile(filename, namespace)
    > 
    >   File
    > "/home/daniel/anaconda3/lib/python3.7/site-packages/spyder_kernels/customize/spydercustomize.py",
    > line 110, in execfile
    >     exec(compile(f.read(), filename, 'exec'), namespace)
    > 
    >   File "/home/daniel/Documentos/Working Papers/Leader
    > traits/Code/20200704 - Modeling
    > Organizing/understanding_coreference.py", line 21, in <module>
    >     ann = client.annotate(text)
    > 
    >   File
    > "/home/daniel/anaconda3/lib/python3.7/site-packages/stanza/server/client.py",
    > line 470, in annotate
    >     r = self._request(text.encode('utf-8'), request_properties, **kwargs)
    > 
    >   File
    > "/home/daniel/anaconda3/lib/python3.7/site-packages/stanza/server/client.py",
    > line 404, in _request
    >     raise AnnotationException(r.text)
    > 
    > AnnotationException: java.lang.RuntimeException:
    > java.lang.IllegalArgumentException: No enum constant
    > edu.stanford.nlp.coref.CorefProperties.CorefAlgorithmType.DETERMINISTIC
   

问题：

1. 为什么我会收到这个确定性错误？

2. 在 Python 中使用 NLP Stanford 的任何代码似乎都比与 Spacy 或 NLTK 相关的代码慢得多。我知道这些其他库中没有共同引用。但是例如，当我使用 import nltk.parse.stanford import StanfordDependencyParser 进行依赖解析时，它比这个 StanfordNLP 库要快得多。有什么方法可以在 Python 中加速这个 CoreNLPClient 吗？

3. 我将使用这个库来处理长文本。将较小的部分与整个文本一起使用会更好吗？长文本会导致共指解析的错误结果（当我使用长文本时，我发现这个共指库的结果非常奇怪）？有最佳尺寸吗？

4. 结果：

统计算法的结果似乎更好。我预计最好的结果将来自神经算法。你是否同意我的观点？统计算法中有 4 个有效提及，而我使用神经算法时只有 2 个。

我错过了什么吗？

Answer 1

1. 您可以在 Java 文档中找到支持的算法列表：link

2. 您可能想启动服务器然后使用它，例如

   # Here's the slowest part—models are being loaded
   client = CoreNLPClient(...)
   
   ann = client.annotate(text)
   
   ...
   
   client.stop()
   

但是我不能给你任何关于 3 和 4 的线索。