GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge

WordNet: A Lexical Database for English

WordNet is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations. It is a computational lexicon of English based on psycholinguistic principles, created and maintained at Princeton University. The main relation among words in WordNet is synonymy, as between the words shut and close or car and automobile. Synonyms--words that denote the same concept and are interchangeable in many contexts--are grouped into unordered sets (synsets). Link to official page: https://wordnet.princeton.edu/ Citation: Princeton University "About WordNet." WordNet. Princeton University. 2010.

GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge

Link to paper: https://arxiv.org/pdf/1908.07245.pdf Word Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, which are widely utilized in knowledge-based methods. Recent studies have shown the effectiveness of incorporating gloss (sense definition) into neural networks for WSD. However, compared with traditional word expert supervised methods, they have not achieved much improvement. In this paper, we focus on how to better leverage gloss knowledge in a supervised neural WSD system. We construct context-gloss pairs and propose three BERT-based models for WSD. We fine-tune the pre-trained BERT model on SemCor3.0 training corpus and the experimental results on several English all-words WSD benchmark datasets show that our approach outperforms the state-of-the-art systems.

Introduction

Introduction on my background and what this podcast will be about.