extract top n words that are most similar to an input word from a text file

You can make use of spacy similarity method, that will calculate cosine similarity between tokens for you. In order to use vectors, load a model with vectors:

import spacy
nlp = spacy.load("en_core_web_md")

text = "I have a text file that contains the content of a web page that I have extracted using BeautifulSoup. I need to find N similar words from the text file based on a given word. The process is as follows"
doc = nlp(text)
words = ['goal', 'soccer']

# compute similarity    
similarities = {}   
for word in words:
    tok = nlp(word)
    similarities[tok.text] ={}
    for tok_ in doc:

# sort
top10 = lambda x: {k: v for k, v in sorted(similarities[x].items(), key=lambda item: item[1], reverse=True)[:10]}

# desired output
{'need': 0.41729581641359625,
 'that': 0.4156277030017712,
 'to': 0.40102258054859163,
 'is': 0.3742535591719576,
 'the': 0.3735002888862756,
 'The': 0.3735002888862756,
 'given': 0.3595024941701789,
 'process': 0.35218102758578645,
 'have': 0.34597281472837316,
 'as': 0.34433650293640194}

Note, (1) if you’re comfortable with gensim, and/or (2) have a word2vec model trained on your text, you can do directly:

word2Vec.most_similar(positive=['goal'], topn=10)

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