The Grammar of Sense: Is word-sense tagging much more than part-of-speech tagging?

TL;DR

This paper argues that large-scale automatic sense tagging (LAST) can achieve high accuracy with less complexity than previously thought, using only part-of-speech information for all open class words.

Contribution

It demonstrates that sense disambiguation for open class words can be effectively performed using simple POS-based methods, challenging assumptions about the complexity of sense tagging.

Findings

92% of open class words can be accurately sense tagged using POS information

Sense disambiguation can be achieved with less computational effort than previously believed

Part-of-speech data contains significant information for sense disambiguation

Abstract

This squib claims that Large-scale Automatic Sense Tagging of text (LAST) can be done at a high-level of accuracy and with far less complexity and computational effort than has been believed until now. Moreover, it can be done for all open class words, and not just carefully selected opposed pairs as in some recent work. We describe two experiments: one exploring the amount of information relevant to sense disambiguation which is contained in the part-of-speech field of entries in Longman Dictionary of Contemporary English (LDOCE). Another, more practical, experiment attempts sense disambiguation of all open class words in a text assigning LDOCE homographs as sense tags using only part-of-speech information. We report that 92% of open class words can be successfully tagged in this way. We plan to extend this work and to implement an improved large-scale tagger, a description of which is included here.