Examining crosslingual word sense disambiguation.

Understanding human language computationally remains a challenge at different levels, phonologically, syntactically and semantically. This thesis attempts to understand human language's ambiguity through the Word Sense Disambiguation (WSD) task. Word Sense Disambiguation (WSD) is the task of determining the correct sense of a word given a context sentence and topic models are statistical models of human language that can discover abstract topics given a collection of documents. This thesis examines the WSD task in a crosslingual manner with the usage of topic models and parallel corpus. The thesis defines a topical crosslingual WSD (Topical CLWSD) task as two subtasks (i) Match and Translate: finding a match of the query sentence in a parallel corpus using topic models that provides the appropriate translation of the target polysemous word (ii) Map: mapping the word-translation pair to disambiguate the concept respectively of the Open Multilingual WordNet. The XLING WSD system has been built to attempt the topical WSD task. Although the XLING system underperforms in the topical WSD task, it serves as a pilot approach to crosslingual WSD in a knowledge-lean manner. Other than the WSD task, the thesis briefly presents updates on the ongoing work to compile multilingual data for the Nanyang Technological University-Multilingual Corpus (NTU-MC). Both the NTU-MC project and the XLING system are related in their attempts to build crosslingual language technologies.Master of Art