A Semantic Unsupervised Learning Approach To Word Sense Disambiguation
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A Semantic Unsupervised Learning Approach to Word Sense Disambiguation
Word Sense Disambiguation (WSD) is the identification of the particular meaning for a word based on the context of its usage. WSD is a complex task that is an important component of language processing and information analysis systems in several fields. The best current methods for WSD rely on human input and are limited to a finite set of words. Complicating matters further, language is dynamic and over time usage changes and new words are introduced. Static definitions created by previously defined analyses become outdated or are inadequate to deal with current usage. Fully automated methods are needed both for sense discovery and for distinguishing the sense being used for a word in context to efficiently realize the benefits of WSD across a broader spectrum of language. Latent Semantic Analysis (LSA) is a powerful automated unsupervised learning system that has not been widely applied in this area. The research described in this proposal will apply advanced LSA techniques in a novel way to the WSD tasks of sense discovery and distinguishing senses in use.
Word Sense Disambiguation
Author: Eneko Agirre
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-11-16
Graeme Hirst University of Toronto Of the many kinds of ambiguity in language, the two that have received the most attention in computational linguistics are those of word senses and those of syntactic structure, and the reasons for this are clear: these ambiguities are overt, their resolution is seemingly essential for any prac- cal application, and they seem to require a wide variety of methods and knowledge-sources with no pattern apparent in what any particular - stance requires. Right at the birth of artificial intelligence, in his 1950 paper “Computing machinery and intelligence”, Alan Turing saw the ability to understand language as an essential test of intelligence, and an essential test of l- guage understanding was an ability to disambiguate; his example involved deciding between the generic and specific readings of the phrase a winter’s day. The first generations of AI researchers found it easy to construct - amples of ambiguities whose resolution seemed to require vast knowledge and deep understanding of the world and complex inference on this kno- edge; for example, Pharmacists dispense with accuracy. The disambig- tion problem was, in a way, nothing less than the artificial intelligence problem itself. No use was seen for a disambiguation method that was less than 100% perfect; either it worked or it didn’t. Lexical resources, such as they were, were considered secondary to non-linguistic common-sense knowledge of the world.
Supervised and Unsupervised Learning for Data Science
This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.