# Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity

## TL;DR

This paper introduces a domain-specific semantic similarity measure that combines Word2Vec embeddings with lexical methods, outperforming existing approaches and demonstrating that lemmatization enhances word embedding performance.

## Contribution

The study proposes a novel union of Word2Vec and lexical methods for domain-specific semantic similarity, showing improved results over traditional methods.

## Key findings

- The combined approach outperforms standalone Word2Vec and lexical methods.
- Lemmatization improves the performance of word embedding methods.
- The proposed method is effective for domain-specific semantic similarity tasks.

## Abstract

Semantic similarity measures are an important part in Natural Language Processing tasks. However Semantic similarity measures built for general use do not perform well within specific domains. Therefore in this study we introduce a domain specific semantic similarity measure that was created by the synergistic union of word2vec, a word embedding method that is used for semantic similarity calculation and lexicon based (lexical) semantic similarity methods. We prove that this proposed methodology out performs word embedding methods trained on generic corpus and methods trained on domain specific corpus but do not use lexical semantic similarity methods to augment the results. Further, we prove that text lemmatization can improve the performance of word embedding methods.