python-3.x - 单词不在词汇表中

Question

第一次使用 word2vec，我正在处理的文件是 XML 格式。我想遍历专利以找到每个标题，然后应用 word2vec 查看是否有相似的词(以指示相似的标题)。

到目前为止，我已经使用元素树解析了 XML 文件以检索每个标题，然后我应用了 sent_tokenizer，然后是 tweet tokenizer 以返回一个句子列表，其中每个单词已被标记化(不确定这是否是最好的方法)。然后，我将标记化的句子放入我的 word2vec 模型中，并用一个词进行测试，看它是否返回了一个向量。这似乎只适用于第一句话中的一个词。我不确定它能识别所有句子吗？

    import numpy as np
    import pandas as pd
    import gensim
    import nltk
    import xml.etree.ElementTree as ET
    from gensim.models.word2vec import Word2Vec
    from nltk.tokenize import word_tokenize
    from nltk.tokenize import sent_tokenize
    from nltk.corpus import stopwords
    from nltk.tokenize import TweetTokenizer, sent_tokenize

    tree = ET.parse('6785.xml')
    root = tree.getroot()

    for child in root.iter("Title"):
        Patent_Title = child.text
        sentence = Patent_Title
        stopWords = set(stopwords.words('english'))
        tokens = nltk.sent_tokenize(sentence)
        print(tokens)

        tokenizer_words = TweetTokenizer()
        tokens_sentences = [tokenizer_words.tokenize(t) for t in tokens]
        #print(tokens_sentences)

        model = gensim.models.Word2Vec(tokens_sentences, min_count=1,size=32)
        words = list(model.wv.vocab)
        print(words)
        print(model['Solar'])

我希望它能够识别句子中的“太阳能”一词并打印出向量，然后我可以寻找相似的词。我收到了错误:

词汇中没有“太阳能”这个词"

Answer 1

仅在第一次循环发生时将错误作为异常处理。

# print(model['Solar'])
try:
    print(model['Solar'])
except Exception as e:
    pass

工作代码:

import numpy as np
import pandas as pd
import gensim
import nltk
import xml.etree.ElementTree as ET
from gensim.models.word2vec import Word2Vec
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize
from nltk.corpus import stopwords
from nltk.tokenize import TweetTokenizer, sent_tokenize

tree = ET.parse('6785.xml')
root = tree.getroot()

for child in root.iter("Title"):
    Patent_Title = child.text
    sentence = Patent_Title
    stopWords = set(stopwords.words('english'))
    tokens = nltk.sent_tokenize(sentence)
    print(tokens)

    tokenizer_words = TweetTokenizer()
    tokens_sentences = [tokenizer_words.tokenize(t) for t in tokens]
    #print(tokens_sentences)

    model = gensim.models.Word2Vec(tokens_sentences, min_count=1,size=32)
    words = list(model.wv.vocab)
    print(words)
    try:
        print(model['Solar'])
    except Exception as e:
        pass