希望从句子中提取复合名词-形容词对.所以，基本上我想要这样的东西: [英] wish to extract compound noun-adjective pairs from a sentence. So, basically I want something like :

问题描述

对于形容词:

"The company's customer service was terrible."
{customer service, terrible}

动词:

"They kept increasing my phone bill"
{phone bill, increasing}

这是来自此帖子

但是我试图使用spacy查找与多令牌短语/复合名词(例如客户服务")相对应的adj和动词.

However I'm trying to find adj and verbs corresponding to multi-token phrases/compound nouns such as "customer service" using spacy.

我不确定如何使用spacy，nltk或任何其他预包装的自然语言处理软件来执行此操作，我将不胜感激！

I'm not sure how to do this with spacy, nltk, or any other prepackaged natural language processing software, and I'd appreciate any help!

推荐答案

对于像这样的简单示例，您可以使用spaCy的依赖关系解析，其中包含一些简单的规则.

For simple examples like this, you can use spaCy's dependency parsing with a few simple rules.

首先，要识别类似于给定示例的多词名词，可以使用"compound"依赖项.在使用spaCy解析文档(例如句子)后，使用令牌的dep_属性来查找其依赖项.

First, to identify multi-word nouns similar to the examples given, you can use the "compound" dependency. After parsing a document (e.g., sentence) with spaCy, use a token's dep_ attribute to find it's dependency.

例如，这个句子有两个复合名词:

For example, this sentence has two compound nouns:

复合依赖项标识复合名词."

"The compound dependency identifies compound nouns."

每个令牌及其依赖性如下所示:

Each token and its dependency is shown below:

import spacy
import pandas as pd
nlp = spacy.load('en')

example_doc = nlp("The compound dependency identifies compound nouns.")
for tok in example_doc:
    print(tok.i, tok, "[", tok.dep_, "]")

>>>0 The [ det ]
>>>1 compound [ compound ]
>>>2 dependency [ nsubj ]
>>>3 identifies [ ROOT ]
>>>4 compound [ compound ]
>>>5 nouns [ dobj ]
>>>6 . [ punct ]
for tok in [tok for tok in example_doc if tok.dep_ == 'compound']: # Get list of 
compounds in doc
    noun = example_doc[tok.i: tok.head.i + 1]
    print(noun)
>>>compound dependency
>>>compound nouns

以下功能适用于您的示例.但是，它可能不适用于更复杂的句子.

The below function works for your examples. However, it will likely not work for more complicated sentences.

adj_doc = nlp("The company's customer service was terrible.")
verb_doc = nlp("They kept increasing my phone bill")

def get_compound_pairs(doc, verbose=False):
    """Return tuples of (multi-noun word, adjective or verb) for document."""
    compounds = [tok for tok in doc if tok.dep_ == 'compound'] # Get list of compounds in doc
    compounds = [c for c in compounds if c.i == 0 or doc[c.i - 1].dep_ != 'compound'] # Remove middle parts of compound nouns, but avoid index errors
    tuple_list = []
    if compounds: 
        for tok in compounds:
            pair_item_1, pair_item_2 = (False, False) # initialize false variables
            noun = doc[tok.i: tok.head.i + 1]
            pair_item_1 = noun
            # If noun is in the subject, we may be looking for adjective in predicate
            # In simple cases, this would mean that the noun shares a head with the adjective
            if noun.root.dep_ == 'nsubj':
                adj_list = [r for r in noun.root.head.rights if r.pos_ == 'ADJ']
                if adj_list:
                    pair_item_2 = adj_list[0] 
                if verbose == True: # For trying different dependency tree parsing rules
                    print("Noun: ", noun)
                    print("Noun root: ", noun.root)
                    print("Noun root head: ", noun.root.head)
                    print("Noun root head rights: ", [r for r in noun.root.head.rights if r.pos_ == 'ADJ'])
            if noun.root.dep_ == 'dobj':
                verb_ancestor_list = [a for a in noun.root.ancestors if a.pos_ == 'VERB']
                if verb_ancestor_list:
                    pair_item_2 = verb_ancestor_list[0]
                if verbose == True: # For trying different dependency tree parsing rules
                    print("Noun: ", noun)
                    print("Noun root: ", noun.root)
                    print("Noun root head: ", noun.root.head)
                    print("Noun root head verb ancestors: ", [a for a in noun.root.ancestors if a.pos_ == 'VERB'])
            if pair_item_1 and pair_item_2:
                tuple_list.append((pair_item_1, pair_item_2))
    return tuple_list

get_compound_pairs(adj_doc)
>>>[(customer service, terrible)]
get_compound_pairs(verb_doc)
>>>[(phone bill, increasing)]
get_compound_pairs(example_doc, verbose=True)
>>>Noun:  compound dependency
>>>Noun root:  dependency
>>>Noun root head:  identifies
>>>Noun root head rights:  []
>>>Noun:  compound nouns
>>>Noun root:  nouns
>>>Noun root head:  identifies
>>>Noun root head verb ancestors:  [identifies]
>>>[(compound nouns, identifies)]

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