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Related Questions
- What are the key differences between cosine similarity and word embeddings in measuring semantic similarity?
- Can you explain how cosine similarity measures similarity between vectors, and how it relates to semantic similarity?
- How do word embeddings, such as Word2Vec or GloVe, capture semantic relationships between words, and what are their advantages over cosine similarity?
- What are the limitations of cosine similarity in evaluating semantic similarity, and how do word embeddings address these limitations?
- Can you provide examples of how cosine similarity and word embeddings can be used to evaluate semantic similarity in different natural language processing tasks?
- How do the dimensions of word embeddings affect the evaluation of semantic similarity, and what are the implications for model design?
- Can you discuss the relationship between word embeddings and other semantic similarity metrics, such as word overlap or lexical chains?
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