# Making Sense of Word Embeddings

arXiv: https://arxiv.org/abs/1708.03390 · HTML: https://mlsanity.com/paper/1708.03390/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2017.

## Built on by (most cited first, 12 of 12)

- [On the Explainability of Natural Language Processing Deep Models](https://mlsanity.com/paper/2210.06929/) (2022)
- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [Driving Context into Text-to-Text Privatization](https://mlsanity.com/paper/2306.01457/) (2023)
- [Recent Trends in Named Entity Recognition (NER)](https://mlsanity.com/paper/2101.11420/) (2021)
- [LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation](https://mlsanity.com/paper/2104.08110/) (2021)
- [Large Scale Substitution-based Word Sense Induction](https://mlsanity.com/paper/2110.07681/) (2021)
- [Multi-sense embeddings through a word sense disambiguation process](https://mlsanity.com/paper/2101.08700/) (2021)
- [Multi-Sense Embeddings for Language Models and Knowledge Distillation](https://mlsanity.com/paper/2504.06036/) (2025)
- [A Survey On Neural Word Embeddings](https://mlsanity.com/paper/2110.01804/) (2021)
- [A Comparative Study of Transformers on Word Sense Disambiguation](https://mlsanity.com/paper/2111.15417/) (2021)
- [An empirical analysis of phrase-based and neural machine translation](https://mlsanity.com/paper/2103.03108/) (2021)
- [Sense representations for Portuguese: experiments with sense embeddings and deep neural language models](https://mlsanity.com/paper/2109.00025/) (2021)

## Abstract (arXiv)

We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.