我想使用Standford CoreNLP提取共指并开始处理预标记文本的依存关系。我最终希望在相关命名实体之间构建图节点和边。我正在python中工作，但是直接使用nltk的java函数来调用“ edu.stanford.nlp.pipeline.StanfordCoreNLP” jar（无论如何nltk都是在后台进行操作）。
我的预先标记文本为以下格式：
PRE-LABELED: During his youth, [PERSON: Alexander III of Macedon] was tutored by [PERSON: Aristotle] until age 16. Following the conquest of [LOCATION: Anatolia], [PERSON: Alexander] broke the power of [LOCATION: Persia] in a series of decisive battles, most notably the battles of [LOCATION: Issus] and [LOCATION: Gaugamela]. He subsequently overthrew [PERSON: Persian King Darius III] and conquered the [ORGANIZATION: Achaemenid Empire] in its entirety.
我试图做的是自己标记我的句子，以IOB格式建立元组列表：[（“ During”，“ O”），（“ his”，“ O”），（“ youth”，“ O”），（“亚历山大”，“ B-PERSON”），（“ III”，“ I-PERSON”），...]
但是，我无法弄清楚如何告诉CoreNLP以该元组列表为起点，构建最初未标记的其他命名实体，并在这些新的，更高质量的标记化句子中找到共指。我显然尝试过简单地剥离标签，让CoreNLP自己执行此操作，但是CoreNLP在查找命名实体方面不如人类标记的预先标记文本好。
我需要如下输出。我知道以这种方式使用依赖关系来获取Edge会很困难，但是我需要看看能得到多远。
DESIRED OUTPUT:
[Person 1]:
Name: Alexander III of Macedon
Mentions:
* "Alexander III of Macedon"; Sent1 [4,5,6,7] # List of tokens
* "Alexander"; Sent2 [6]
* "He"; Sent3 [1]
Edges:
* "Person 2"; "tutored by"; "Aristotle"
[Person 2]:
Name: Aristotle
[....]
我如何向CoreNLP提供一些预先定义的命名实体，并仍然获得有关其他命名实体，共指以及基本依赖项的帮助？
P.S。请注意，这不是NLTK Named Entity Recognition with Custom Data的副本。我不是在尝试使用预先标记的NER来训练新的分类器，而是在运行共指（包括提及）和对给定句子的依存关系时尝试将CoreNLP添加到我自己的分类器中。
答案 0 :(得分：0)
答案是使用Additional TokensRegexNER Rules创建一个Rules文件。
我用了一个正则表达式将标签名称分组。由此，我构建了一个规则临时文件，并使用-ner.additional.regexner.mapping mytemprulesfile传递给corenlp jar。
Alexander III of Macedon PERSON PERSON,LOCATION,ORGANIZATION,MISC
Aristotle PERSON PERSON,LOCATION,ORGANIZATION,MISC
Anatolia LOCATION PERSON,LOCATION,ORGANIZATION,MISC
Alexander PERSON PERSON,LOCATION,ORGANIZATION,MISC
Persia LOCATION PERSON,LOCATION,ORGANIZATION,MISC
Issus LOCATION PERSON,LOCATION,ORGANIZATION,MISC
Gaugamela LOCATION PERSON,LOCATION,ORGANIZATION,MISC
Persian King Darius III PERSON PERSON,LOCATION,ORGANIZATION,MISC
Achaemenid Empire ORGANIZATION PERSON,LOCATION,ORGANIZATION,MISC
我已将此列表对齐以提高可读性，但这是制表符分隔的值。
一个有趣的发现是，某些预先标记了多个单词的实体保留了最初标记的多个单词，而在没有规则文件的情况下运行corenlp有时会将这些标记拆分为单独的实体。
我本来想专门标识命名实体令牌，弄清楚它会使共同引用更加容易，但是我想现在就可以了。无论如何，实体名称在一个文档中有多少次相同但没有关联？
示例 （执行大约需要70秒）
import os, re, tempfile, json, nltk, pprint
from subprocess import PIPE
from nltk.internals import (
find_jar_iter,
config_java,
java,
_java_options,
find_jars_within_path,
)
def ExtractLabeledEntitiesByRegex( text, regex ):
rgx = re.compile(regex)
nelist = []
for mobj in rgx.finditer( text ):
ne = mobj.group('ner')
try:
tag = mobj.group('tag')
except IndexError:
tag = 'PERSON'
mstr = text[mobj.start():mobj.end()]
nelist.append( (ne,tag,mstr) )
cleantext = rgx.sub("\g<ner>", text)
return (nelist, cleantext)
def GenerateTokensNERRules( nelist ):
rules = ""
for ne in nelist:
rules += ne[0]+'\t'+ne[1]+'\tPERSON,LOCATION,ORGANIZATION,MISC\n'
return rules
def GetEntities( origtext ):
nelist, cleantext = ExtractLabeledEntitiesByRegex( origtext, '(\[(?P<tag>[a-zA-Z]+)\:\s*)(?P<ner>(\s*\w)+)(\s*\])' )
origfile = tempfile.NamedTemporaryFile(mode='r+b', delete=False)
origfile.write( cleantext.encode('utf-8') )
origfile.flush()
origfile.seek(0)
nerrulefile = tempfile.NamedTemporaryFile(mode='r+b', delete=False)
nerrulefile.write( GenerateTokensNERRules(nelist).encode('utf-8') )
nerrulefile.flush()
nerrulefile.seek(0)
java_options='-mx4g'
config_java(options=java_options, verbose=True)
stanford_jar = '../stanford-corenlp-full-2018-10-05/stanford-corenlp-3.9.2.jar'
stanford_dir = os.path.split(stanford_jar)[0]
_classpath = tuple(find_jars_within_path(stanford_dir))
cmd = ['edu.stanford.nlp.pipeline.StanfordCoreNLP',
'-annotators','tokenize,ssplit,pos,lemma,ner,parse,coref,coref.mention,depparse,natlog,openie,relation',
'-ner.combinationMode','HIGH_RECALL',
'-ner.additional.regexner.mapping',nerrulefile.name,
'-coref.algorithm','neural',
'-outputFormat','json',
'-file',origfile.name
]
# java( cmd, classpath=_classpath, stdout=PIPE, stderr=PIPE )
stdout, stderr = java( cmd, classpath=_classpath, stdout=PIPE, stderr=PIPE ) # Couldn't get working- stdin=textfile
PrintJavaOutput( stdout, stderr )
origfilenametuple = os.path.split(origfile.name)
jsonfilename = origfilenametuple[len(origfilenametuple)-1] + '.json'
os.unlink( origfile.name )
os.unlink( nerrulefile.name )
origfile.close()
nerrulefile.close()
with open( jsonfilename ) as jsonfile:
jsondata = json.load(jsonfile)
currentid = 0
entities = []
for sent in jsondata['sentences']:
for thisentity in sent['entitymentions']:
tag = thisentity['ner']
if tag == 'PERSON' or tag == 'LOCATION' or tag == 'ORGANIZATION':
entity = {
'id':currentid,
'label':thisentity['text'],
'tag':tag
}
entities.append( entity )
currentid += 1
return entities
#### RUN ####
corpustext = "During his youth, [PERSON:Alexander III of Macedon] was tutored by [PERSON: Aristotle] until age 16. Following the conquest of [LOCATION: Anatolia], [PERSON: Alexander] broke the power of [LOCATION: Persia] in a series of decisive battles, most notably the battles of [LOCATION: Issus] and [LOCATION: Gaugamela]. He subsequently overthrew [PERSON: Persian King Darius III] and conquered the [ORGANIZATION: Achaemenid Empire] in its entirety."
entities = GetEntities( corpustext )
for thisent in entities:
pprint.pprint( thisent )
输出
{'id': 0, 'label': 'Alexander III of Macedon', 'tag': 'PERSON'}
{'id': 1, 'label': 'Aristotle', 'tag': 'PERSON'}
{'id': 2, 'label': 'his', 'tag': 'PERSON'}
{'id': 3, 'label': 'Anatolia', 'tag': 'LOCATION'}
{'id': 4, 'label': 'Alexander', 'tag': 'PERSON'}
{'id': 5, 'label': 'Persia', 'tag': 'LOCATION'}
{'id': 6, 'label': 'Issus', 'tag': 'LOCATION'}
{'id': 7, 'label': 'Gaugamela', 'tag': 'LOCATION'}
{'id': 8, 'label': 'Persian King Darius III', 'tag': 'PERSON'}
{'id': 9, 'label': 'Achaemenid Empire', 'tag': 'ORGANIZATION'}
{'id': 10, 'label': 'He', 'tag': 'PERSON'}