# SensPick: Sense Picking for Word Sense Disambiguation

arXiv: https://arxiv.org/abs/2102.05260 · HTML: https://mlsanity.com/paper/2102.05260/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [Leveraging Knowledge Bases in LSTMs for Improving Machine Reading](https://mlsanity.com/paper/1902.09091/) (2019)
- [Generative Adversarial Network for Abstractive Text Summarization](https://mlsanity.com/paper/1711.09357/) (2017)
- [Incorporating Glosses into Neural Word Sense Disambiguation](https://mlsanity.com/paper/1805.08028/) (2018)
- [Word Sense Disambiguation using a Bidirectional LSTM](https://mlsanity.com/paper/1606.03568/) (2016)
- [Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection](https://mlsanity.com/paper/2011.07208/) (2020)
- [A Large-Scale Multilingual Disambiguation of Glosses](https://mlsanity.com/paper/1608.06718/) (2016)
- [Knowledge-based Word Sense Disambiguation using Topic Models](https://mlsanity.com/paper/1801.01900/) (2018)
- [Word sense disambiguation: a complex network approach](https://mlsanity.com/paper/1606.07950/) (2016)

## Built on by (most cited first, 1 of 1)

- [DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning](https://mlsanity.com/paper/2109.02874/) (2021)

## Abstract (arXiv)

Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context. Neural network-based WSD approaches rely on a sense-annotated corpus since they do not utilize lexical resources. In this study, we utilize both context and related gloss information of a target word to model the semantic relationship between the word and the set of glosses. We propose SensPick, a type of stacked bidirectional Long Short Term Memory (LSTM) network to perform the WSD task. The experimental evaluation demonstrates that SensPick outperforms traditional and state-of-the-art models on most of the benchmark datasets with a relative improvement of 3.5% in F-1 score. While the improvement is not significant, incorporating semantic relationships brings SensPick in the leading position compared to others.