我正在尝试了解 Coreference NLP Stanford 工具。 这是我的代码，它正在工作：
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：
- 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
- 神经
this giant man from the Northeast , who had never worn an overcoat or experienced a change of seasons , his
确定性（我得到下面的错误）
> 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：
为什么我会遇到确定性错误？
在 Python 中使用 NLP 斯坦福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
import nltk.parse.stanford import StanfordDependencyParser用于依赖解析时，它比这个 StanfordNLP 库快得多。有什么方法可以在 Python 中加速这个 CoreNLPClient 吗？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？
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？