nltk.pos_tag_sents

`nltk.pos_tag_sents`是Python自然语言处理工具包NLTK（Natural Language Toolkit）中的一个函数，用于对句子序列（sentences）进行词性标注（Part-of-Speech tagging）。词性标注是将文本中的单词与其对应的词汇类别相匹配的过程，比如名词、动词、形容词等。这个函数接受一个由句子组成的列表作为输入，然后对每个句子中的单词进行标注，并返回一个二维列表，其中每个内部列表表示一个句子及其对应的词性标记结果。

例如：

import nltk
sentences = [['This', 'is', 'a', 'sentence'], ['POS', 'tagging', 'example']]
pos_tags = nltk.pos_tag_sents(sentences)

`pos_tags`将会是一个像这样的结构：

[[(‘This’, ‘DT’), (‘is’, ‘VBZ’), (‘a’, ‘DT’), (‘sentence’, ‘NN’)], [(‘POS’, ‘NNP’), (‘tagging’, ‘VBG’), (‘example’, ‘NN’)]]

每个元素的第一个值是单词，第二个值是词性标签。

相关问题

t2.train(train_sents)Traceback (most recent call last): File "<input>", line 1, in <module> AttributeError: 'BigramTagger' object has no attribute 'train'如何改正 完整代码如下：import nltk import random from nltk.corpus import brown from nltk import pos_tag, word_tokenize from nltk.tag import DefaultTagger, UnigramTagger, BigramTagger brown_tagged_sents = brown.tagged_sents(categories='news') size = int(len(brown_tagged_sents) * 0.9) train_sents = brown_tagged_sents[:size] test_sents = brown_tagged_sents[size:] t0 = DefaultTagger('NN') t1 = UnigramTagger(train_sents, backoff=t0) t2 = BigramTagger(train_sents, backoff=t1) t2.train(train_sents) Traceback (most recent call last): File "<input>", line 1, in <module> AttributeError: 'BigramTagger' object has no attribute 'train'

The error message indicates that the `BigramTagger` object does not have the `train` method. This is because `BigramTagger` is already trained during initialization.

To fix this error, you can remove the `t2.train(train_sents)` line and directly use the `t2` tagger to tag new sentences. For example:

sent = "This is a test sentence"
tokens = word_tokenize(sent)
tags = t2.tag(tokens)
print(tags)