使用自定义数据进行NLTK命名实体识别

import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk

# 下载必要的数据
nltk.download('punkt')
nltk.download('maxent_ne_chunker')
nltk.download('words')
nltk.download('averaged_perceptron_tagger')

# 示例文本
text = "Barack Obama was born in Hawaii. He was the 44th President of the United States."

# 分词
tokens = word_tokenize(text)

# 词性标注
tagged = pos_tag(tokens)

# 命名实体识别
entities = ne_chunk(tagged)

# 打印结果
print(entities)

import nltk
from nltk.tag import StanfordNERTagger
from nltk.tokenize import word_tokenize

# 下载必要的数据
nltk.download('punkt')

# 示例自定义数据
custom_data = [
    ("Barack Obama", "PERSON"),
    ("Hawaii", "LOCATION"),
    ("United States", "LOCATION")
]

# 分词
tokenized_data = [(word_tokenize(sentence), [tag for word, tag in sentence]) for sentence, _ in custom_data]

# 训练模型
st = StanfordNERTagger('path/to/stanford-ner.jar', 'path/to/classifiers/english.all.3class.distsim.crf.ser.gz')
st.train(tokenized_data, 'path/to/output.model')

# 使用训练好的模型进行NER
text = "Barack Obama was born in Hawaii."
tokens = word_tokenize(text)
entities = st.tag(tokens)
print(entities)