句子结构识别-虚假

Question

我打算使用spacy和textacy来识别英语中的句子结构。

例如： 猫坐在垫子上-SVO，猫跳了起来，拿起了饼干-SVV0。那只猫吃了饼干和饼干。-SVOO。

该程序应该读取一个段落并以SVO，SVOO，SVVO或其他自定义结构返回每个句子的输出。

到目前为止的努力：

# -*- coding: utf-8 -*-
#!/usr/bin/env python
from __future__ import unicode_literals
# Load Library files
import en_core_web_sm
import spacy
import textacy
nlp = en_core_web_sm.load()
SUBJ = ["nsubj","nsubjpass"] 
VERB = ["ROOT"] 
OBJ = ["dobj", "pobj", "dobj"] 
text = nlp(u'The cat sat on the mat. The cat jumped and picked up the biscuit. The cat ate biscuit and cookies.')
sub_toks = [tok for tok in text if (tok.dep_ in SUBJ) ]
obj_toks = [tok for tok in text if (tok.dep_ in OBJ) ]
vrb_toks = [tok for tok in text if (tok.dep_ in VERB) ]
text_ext = list(textacy.extract.subject_verb_object_triples(text))
print("Subjects:", sub_toks)
print("VERB :", vrb_toks)
print("OBJECT(s):", obj_toks)
print ("SVO:", text_ext)

输出：

(u'Subjects:', [cat, cat, cat])
(u'VERB :', [sat, jumped, ate])
(u'OBJECT(s):', [mat, biscuit, biscuit])
(u'SVO:', [(cat, ate, biscuit), (cat, ate, cookies)])

• 问题1：SVO被覆盖。为什么？
• 问题2：如何识别句子SVOO SVO SVVO等？

编辑1：

我正在概念化的某种方法。

from __future__ import unicode_literals
import spacy,en_core_web_sm
import textacy
nlp = en_core_web_sm.load()
sentence = 'I will go to the mall.'
doc = nlp(sentence)
chk_set = set(['PRP','MD','NN'])
result = chk_set.issubset(t.tag_ for t in doc)
if result == False:
    print "SVO not identified"
elif result == True: # shouldn't do this
    print "SVO"
else:
    print "Others..."

编辑2：

取得进一步进展

from __future__ import unicode_literals
import spacy,en_core_web_sm
import textacy
nlp = en_core_web_sm.load()
sentence = 'The cat sat on the mat. The cat jumped and picked up the biscuit. The cat ate biscuit and cookies.'
doc = nlp(sentence)
print(" ".join([token.dep_ for token in doc]))

电流输出：

det nsubj ROOT prep det pobj punct det nsubj ROOT cc conj prt det dobj punct det nsubj ROOT dobj cc conjpunct

预期产量：

SVO SVVO SVOO

想法是将依赖项标签分解为简单的主语-动词和宾语模型。

如果没有其他选择，可以考虑使用正则表达式来实现。但这是我的最后选择。

编辑3：

在研究了此链接后，得到了一些改进。

def testSVOs():
    nlp = en_core_web_sm.load()
    tok = nlp("The cat sat on the mat. The cat jumped for the biscuit. The cat ate biscuit and cookies.")
    svos = findSVOs(tok)
    print(svos)

电流输出：

[(u'cat', u'sat', u'mat'), (u'cat', u'jumped', u'biscuit'), (u'cat', u'ate', u'biscuit'), (u'cat', u'ate', u'cookies')]

---

预期产量：

我期待句子的符号。尽管我能够提取SVO上如何将其转换为SVO表示法。它更多是模式识别，而不是句子内容本身。

SVO SVO SVOO

Answer 1

问题 1：SVO 被覆盖。为什么？

这是textacy问题。这部分效果不太好，请参阅此博客

问题2：如何识别句子为SVOO SVO SVVO等？

您应该解析依赖树。SpaCy提供了信息，您只需编写一组规则即可使用.head、.left和属性.right将其提取出来。.children

>>for word in text: 
    print('%10s %5s %10s %10s %s'%(word.text, word.tag_, word.dep_, word.pos_, word.head.text_))

        The    DT        det        DET cat 
        cat    NN      nsubj       NOUN sat 
        sat   VBD       ROOT       VERB sat 
         on    IN       prep        ADP sat 
        the    DT        det        DET mat
        mat    NN       pobj       NOUN on 
          .     .      punct      PUNCT sat 
         of    IN       ROOT        ADP of 
        the    DT        det        DET lab
        art    NN   compound       NOUN lab
        lab    NN       pobj       NOUN of 
          .     .      punct      PUNCT of 
        The    DT        det        DET cat 
        cat    NN      nsubj       NOUN jumped 
     jumped   VBD       ROOT       VERB jumped 
        and    CC         cc      CCONJ jumped 
     picked   VBD       conj       VERB jumped 
         up    RP        prt       PART picked 
        the    DT        det        DET biscuit
    biscuit    NN       dobj       NOUN picked 
          .     .      punct      PUNCT jumped 
        The    DT        det        DET cat 
        cat    NN      nsubj       NOUN ate 
        ate   VBD       ROOT       VERB ate 
    biscuit    NN       dobj       NOUN ate 
        and    CC         cc      CCONJ biscuit 
    cookies   NNS       conj       NOUN biscuit 
          .     .      punct      PUNCT ate 

我建议您查看此代码，只需添加pobj到列表中OBJECTS，您就会涵盖 SVO 和 SVOO。只要稍微摆弄一下，您也可以获得 SVVO。