我想从每个句子中提取名词和复合名词，包括连字符，如下所示。如果它包含连字符，我需要用连字符提取它。

{The T-shirt is old.: ['T-shirt'], 
I bought the computer and the new web-cam.: ['computer', 'web-cam'], 
I bought the computer and the new web camera.: ['computer', 'web camera']}

电流输出低于。在复合名词的第一个单词上有标签“复合”，但我现在无法提取我所期望的。

T T PROPN NNP compound X True False
shirt shirt NOUN NN nsubj xxxx True False
computer computer NOUN NN dobj xxxx True False
web web NOUN NN compound xxx True False
cam cam NOUN NN conj xxx True False
computer computer NOUN NN dobj xxxx True False
web web NOUN NN compound xxx True False
camera camera NOUN NN conj xxxx True False

{The T-shirt is old.: ['T -', 'T', 'T -', 'shirt'], 
I bought the computer and the new web-cam.: ['web -', 'computer', 'web -', 'web', 'web -', 'cam'], 
I bought the computer and the new web camera.: ['web camera', 'computer', 'web camera', 'web', 'web camera', 'camera']}

我使用NLP库spaCy来区分名词和复合名词。希望听听大家的建议如何修复当前代码。

import spacy
nlp = spacy.load("en_core_web_sm")

texts =  ["The T-shirt is old.", "I bought the computer and the new web-cam.", "I bought the computer and the new web camera."]

nouns = []*len(texts)
dic = {k: v for k, v in zip(texts, nouns)}

for i in range(len(texts)):
    text = nlp(texts[i])
    words = []
    for word in text:
        if word.pos_ == 'NOUN'or word.pos_ == 'PROPN':
            print(word.text, word.lemma_, word.pos_, word.tag_, word.dep_,
                word.shape_, word.is_alpha, word.is_stop)

            #compound words
            for j in range(len(text)):
                    token = text[j]
                    if token.dep_ == 'compound':
                        if j < len(text)-1:
                            nexttoken = text[j+1]
                            words.append(str(token.text + ' ' + nexttoken.text))


            else:
                words.append(word.text)
    dic[text] = words       
print(dic)

蟒蛇 3.7.4

spaCy 版本 2.3.2