Word Sense Disambiguation based on Semantic Density
Rada F. Mihalcea, DAN I. MOLDOVAN · University of North Texas Digital Library (University of North Texas) · 1998
This paper presents a Word Sense Disambiguation method based on the idea of semantic density between words. The disambiguation is done in the context of WordNet. The Internet is used as a raw corpora to provide statistical information for word associations. A metric is introduced and used to measure the semantic density and to rank all possible combinations of the senses of two words. This method provides a precision of 58% in indicating the correct sense for both words at the same time. The precision increases as we consider more choices: 70% for top two ranked and 73% for top three ranked. 1 Introduction Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing. Its solution impacts other tasks such as discourse, reference resolution, coherence, inference and others. WSD methods can be broadly classified into three types: 1. WSD that make use of the information provided by machine readable dictionaries (Cowie et al.1992), (Miller et al.1994), (Agirre and Rig...