Sense Tagging: Semantic Tagging with a Lexicon

TL;DR

This paper presents a semantic tagging system that assigns correct senses to words in text by combining simple algorithms, demonstrating high accuracy in initial tests.

Contribution

It introduces a novel approach to sense tagging using a combination of simple, independent methods and provides a prototype system with promising results.

Findings

Correctly tagged 86% of polysemous words in tests

Supports combining simple methods for effective sense tagging

Prototype demonstrates feasibility of the approach

Abstract

Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of semantic tagging by category or type. We discuss which recent word sense disambiguation algorithms are appropriate for sense tagging. It is our belief that sense tagging can be carried out effectively by combining several simple, independent, methods and we include the design of such a tagger. A prototype of this system has been implemented, correctly tagging 86% of polysemous word tokens in a small test set, providing evidence that our hypothesis is correct.