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

arXiv: https://arxiv.org/abs/1706.01967 · HTML: https://mlsanity.com/paper/1706.01967/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2017.

## Built on by (most cited first, 8 of 8)

- [Synthesis and Evaluation of a Domain-specific Large Data Set for Dungeons & Dragons](https://mlsanity.com/paper/2212.09080/) (2022)
- [Unsupervised Simplification of Legal Texts](https://mlsanity.com/paper/2209.00557/) (2022)
- [Critical Sentence Identification in Legal Cases Using Multi-Class Classification](https://mlsanity.com/paper/2111.05721/) (2021)
- [Comparative Analysis of Named Entity Recognition in the Dungeons and Dragons Domain](https://mlsanity.com/paper/2309.17171/) (2023)
- [ScheduleMe: Multi-Agent Calendar Assistant](https://mlsanity.com/paper/2509.25693/) (2025)
- [Translating Under Pressure: Domain-Aware LLMs for Crisis Communication](https://mlsanity.com/paper/2604.26597/) (2026)
- [Fine Tuning Named Entity Extraction Models for the Fantasy Domain](https://mlsanity.com/paper/2402.10662/) (2024)
- [SHADE: Semantic Hypernym Annotator for Domain-specific Entities -- DnD Domain Use Case](https://mlsanity.com/paper/2407.00407/) (2024)

## Abstract (arXiv)

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.