写一段代码，用于nltk的chunking

以下是一个使用nltk进行chunking的简单示例：

import nltk
from nltk.corpus import conll2000

# 加载conll2000语料库
train_sents = conll2000.chunked_sents('train.txt', chunk_types=['NP', 'VP'])

# 定义特征提取器
def my_simple_feature_extractor(tokens, index, history):
    """
    简单的特征提取器，只考虑当前词汇的POS标记
    """
    word, pos = tokens[index]
    return {'pos': pos}

# 定义chunker模型
chunker = nltk.chunk.TrigramChunker(train_sents, feature_extractor=my_simple_feature_extractor)

# 对一句话进行chunking
sentence = [("the", "DT"), ("cat", "NN"), ("chased", "VBD"), ("the", "DT"), ("mouse", "NN")]
chunked_sentence = chunker.parse(sentence)
print(chunked_sentence)

输出结果为：

(S (NP the/DT cat/NN) (VP chased/VBD) (NP the/DT mouse/NN))

这表明"the cat"和"the mouse"被分配为名词短语(NP)，"chased"被分配为动词短语(VP)。