Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment

Dijkstra-WSA: A Graph-Based Approach to Word Sense Alignment

In this paper, we present Dijkstra-WSA, a novel graph-based algorithm for word sense alignment. We evaluate it on four different pairs of lexical-semantic resources with different characteristics (WordNet-OmegaWiki, WordNet-Wiktionary, GermaNet-Wiktionary and WordNet-Wikipedia) and show that it achieves competitive performance on 3 out of 4 datasets. Dijkstra-WSA outperforms the state of the ar...

Graph-based approaches to word sense induction

A Class-based Approach to Word Alignment

This paper presents an algorithm capable of identifying the translation for each word in a bilingual corpus. Previously proposed methods rely heavily on word-based statistics. Under a word-based approach, frequent words with a consistent translation can be aligned at a high rate of precision. However, words that are less frequent or exhibit diverse translations generally do not have statistical...

Concept-Based Approach to Word-Sense Disambiguation

The task of automatically determining the correct sense of a polysemous word has remained a challenge to this day. In our research, we introduce Concept-Based Disambiguation (CBD), a novel framework that utilizes recent semantic analysis techniques to represent both the context of the word and its senses in a high-dimensional space of natural concepts. The concepts are retrieved from a vast enc...

A Concept-based Adaptive Approach to Word Sense Disambiguation

Word sense disambiguation for unrestricted text is one of the most difficult tasks in the fields of computational linguistics. The crux of the problem is to discover a model that relates the intended sense of a word with its context. This paper describes a general framework for adaptive conceptual word sense disambiguation. Central to this WSD framework is the sense division and semantic relati...