分别使用WordNet算法和Lesk算法进行英文消歧义的Python实现

WordNet算法和Lesk算法都是用于英文消歧义的算法，它们可以帮助我们确定一个单词在特定上下文中的含义。下面是两种算法的Python实现：

1. WordNet算法实现：

from nltk.corpus import wordnet

def wordnet_disambiguate(word, sentence):
    # 获取单词的同义词集
    synsets = wordnet.synsets(word)
    best_sense = None
    max_overlap = 0
    context = set(sentence)
    for synset in synsets:
        # 获取同义词集的定义和例句
        definition = set(synset.definition().split())
        examples = set(" ".join(synset.examples()).split())
        # 计算上下文与定义和例句的重叠度
        overlap = len(context.intersection(definition)) + len(context.intersection(examples))
        # 选择最佳的同义词集
        if overlap > max_overlap:
            max_overlap = overlap
            best_sense = synset
    return best_sense.definition()

sentence = "I went to the bank to deposit my money."
word = "bank"
print(wordnet_disambiguate(word, sentence.split()))

2. Lesk算法实现：

from nltk.corpus import wordnet
from nltk.corpus import stopwords

def lesk_disambiguate(word, sentence):
    # 获取单词的同义词集
    synsets = wordnet.synsets(word)
    best_sense = None
    max_overlap = 0
    context = set(sentence)
    for synset in synsets:
        # 获取同义词集的定义和例句
        definition = set(synset.definition().split())
        examples = set(" ".join(synset.examples()).split())
        # 去除停用词
        stop_words = set(stopwords.words('english'))
        definition = definition.difference(stop_words)
        examples = examples.difference(stop_words)
        # 计算上下文与定义和例句的重叠度
        overlap = len(context.intersection(definition)) + len(context.intersection(examples))
        # 选择最佳的同义词集
        if overlap > max_overlap:
            max_overlap = overlap
            best_sense = synset
    return best_sense.definition()

sentence = "I went to the bank to deposit my money."
word = "bank"
print(lesk_disambiguate(word, sentence.split()))

这两种算法的实现过程大致相同，不同之处在于Lesk算法会去除停用词。在使用时只需传入待消歧义的单词和句子即可。