Question

我正在尝试将股票代码添加到识别为ORG实体的字符串中。对于每个符号，我都这样做：

nlp.matcher.add(symbol, u'ORG', {}, [[{u'orth': symbol}]])

我可以看到这个符号被添加到模式中：

print "Patterns:", nlp.matcher._patterns

但添加前无法识别添加前未识别的任何符号。显然，这些代币已经存在于词汇表中（这就是为什么词汇长度不会改变）。

我应该做些什么？我错过了什么？

由于

这是我的示例代码：

＆＃34;练习将股票代码符号添加为ORG实体的简要摘要＆＃34;

from spacy.en import English
import spacy.en
from spacy.attrs import ORTH, TAG, LOWER, IS_ALPHA, FLAG63
import os
import csv
import sys

nlp = English()  #Load everything for the English model

print "Before nlp vocab length", len(nlp.matcher.vocab)

symbol_list = [u"CHK", u"JONE", u"NE", u"DO",  u"ESV"]

txt =  u"""drive double-digit rallies in Chesapeake Energy (NYSE: CHK), (NYSE: NE), (NYSE: DO), (NYSE: ESV), (NYSE: JONE)"""# u"""Drive double-digit rallies in Chesapeake Energy (NYSE: CHK), Noble Corporation (NYSE:NE), Diamond Offshore (NYSE:DO), Ensco (NYSE:ESV), and Jones Energy (NYSE: JONE)"""
before = nlp(txt)
for tok in before:   #Before adding entities
    print tok, tok.orth, tok.tag_, tok.ent_type_

for symbol in symbol_list:
    print "adding symbol:", symbol
    print "vocab length:", len(nlp.matcher.vocab)
    print "pattern length:", nlp.matcher.n_patterns
    nlp.matcher.add(symbol, u'ORG', {}, [[{u'orth': symbol}]])


print "Patterns:", nlp.matcher._patterns
print "Entities:", nlp.matcher._entities
for ent in nlp.matcher._entities:
    print ent.label

tokens = nlp(txt)

print "\n\nAfter:"
print "After nlp vocab length", len(nlp.matcher.vocab)

for tok in tokens:
    print tok, tok.orth, tok.tag_, tok.ent_type_

Answer 1

这是基于docs：

的工作示例

import spacy

nlp = spacy.load('en')

def merge_phrases(matcher, doc, i, matches):
    '''
    Merge a phrase. We have to be careful here because we'll change the token indices.
    To avoid problems, merge all the phrases once we're called on the last match.
    '''
    if i != len(matches)-1:
        return None
    spans = [(ent_id, label, doc[start : end]) for ent_id, label, start, end in matches]
    for ent_id, label, span in spans:
        span.merge('NNP' if label else span.root.tag_, span.text, nlp.vocab.strings[label])

matcher = spacy.matcher.Matcher(nlp.vocab)
matcher.add(entity_key='stock-nyse', label='STOCK', attrs={}, specs=[[{spacy.attrs.ORTH: 'NYSE'}]], on_match=merge_phrases)
matcher.add(entity_key='stock-esv', label='STOCK', attrs={}, specs=[[{spacy.attrs.ORTH: 'ESV'}]], on_match=merge_phrases)
doc = nlp(u"""drive double-digit rallies in Chesapeake Energy (NYSE: CHK), (NYSE: NE), (NYSE: DO), (NYSE: ESV), (NYSE: JONE)""")
matcher(doc)
print(['%s|%s' % (t.orth_, t.ent_type_) for t in doc])

- ＆GT;

['drive|', 'double|', '-|', 'digit|', 'rallies|', 'in|', 'Chesapeake|ORG', 'Energy|ORG', '(|', 'NYSE|STOCK', ':|', 'CHK|', ')|', ',|', '(|', 'NYSE|STOCK', ':|', 'NE|GPE', ')|', ',|', '(|', 'NYSE|STOCK', ':|', 'DO|', ')|', ',|', '(|', 'NYSE|STOCK', ':|', 'ESV|STOCK', ')|', ',|', '(|', 'NYSE|STOCK', ':|', 'JONE|ORG', ')|']

NYSE和ESV现在标有STOCK实体类型。基本上，在每次匹配时，您应手动合并令牌和/或分配所需的实体类型。还有acceptor功能，允许您在匹配时过滤/拒绝匹配。

如何在spacy nlp中添加新实体（ORG）实例

1 个答案: