使用NLP匹配2个关键词列表

Question

我有两个列表，我想确定哪些元素在列表中是常见的(在含义或上下文上相同或相似)。我们应该使用哪种NLP算法。

list-1= [US, Apple, Trump, Biden, Mango, French, German]

list-2= [State, iphone, ipad, ipod, president, person, Fruit, Language, Country]

Answer 1

最简单的实现是使用以下步骤：

Step 1 : Iterate through both the list 
Step 2 : Calculate the Cossine Similarity between each word in list1 with list2
Step 3 : Decide the threshold on cossine similarity. Higher means stricter

守则如下：

list_1 = [ US, Apple, Trump, Biden, Mango, French, German]
list_2 = [State, iphone, ipad, ipod, president, person, Fruit, Language, Country]


# Download the package and model : 
from gensim.models import Word2Vec

similarity_dict = {}
for word_list1 in list_1:
    for word_list2 in list_2:
         model = Word2Vec.load(path/to/your/model)
         cosine_similarity = model.wv.similarity(word_list1, word_list2)

优点：

1. 易于实现
2. 使用Word2vec aso作为word2vec确保上下文非常可靠
3. 易于理解和

缺点：

1. 该代码的复杂度为O(n*n)
2. 它不适用于word2vec中词汇量不足的单词