NLP Semantic Similarity: Identifying Synonyms in a Large Corpus of Words

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Identifying synonyms in a large corpus of words involves natural language processing (NLP) techniques and various methods to capture semantic similarity between words. Here are several approaches that can be used:

1. Word Embeddings:

• Train word embeddings using methods like Word2Vec, GloVe, or FastText. These methods represent words as dense vectors in a continuous vector space. Similar words are expected to have similar vector representations.
• Calculate cosine similarity between word vectors to measure their similarity. Words with high cosine similarity are likely to be synonyms.

2. Distributional Semantics:

• Analyze the distributional patterns of words in the corpus. Words that appear in similar contexts or have similar neighbors are likely to be synonyms.
• Techniques like Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA) can be applied to capture the underlying semantic structure of the corpus.

3. WordNet:

• WordNet is a lexical database that relates words to one another in terms of synonyms, hypernyms, hyponyms, etc. It can be used to identify synonyms.