Knowledge-based Word Sense Disambiguation using Topic Models

TL;DR

This paper introduces a scalable, unsupervised knowledge-based Word Sense Disambiguation method using topic models, leveraging entire documents as context and outperforming existing systems on multiple datasets.

Contribution

The paper presents a novel variant of LDA that incorporates synset information and WordNet priors, enabling efficient use of full document context for WSD.

Findings

Outperforms state-of-the-art unsupervised WSD systems on multiple datasets

Scales linearly with the number of words, allowing full document context utilization

Demonstrates significant accuracy improvements in disambiguation tasks

Abstract

Word Sense Disambiguation is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. Typically WSD systems use the sentence or a small window of words around the target word as the context for disambiguation because their computational complexity scales exponentially with the size of the context. In this paper, we leverage the formalism of topic model to design a WSD system that scales linearly with the number of words in the context. As a result, our system is able to utilize the whole document as the context for a word to be disambiguated. The proposed method is a variant of Latent Dirichlet Allocation in which the topic proportions for a document are replaced by synset proportions. We further utilize the information in the WordNet by assigning a non-uniform prior to synset distribution over words and a logistic-normal prior for document distribution over synsets. We evaluate the proposed method on Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English All-Word WSD datasets and show that it outperforms the state-of-the-art unsupervised knowledge-based WSD system by a significant margin.