In natural languages, a word can take on different meanings in different contexts. Word sense disambiguation (WSD) refers to the task of determining the correct meaning or sense of a word in context

Lian Tze, Lim

Tang, Enya Kong

English

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Lost in Translation: Word Sense DisambiguationLim Lian TzeComputer Aided Translation UnitSchool of Computer Sciences UniversitiSalns Malaysia11800 Minden, Penang, Malaysia+60(0) 46533888 ext 2693liantze@cs.usm.myABSTRACTIn natural languages, a word can take on different meanings indifferent contexts. Word sense disambiguation (WSD) refersto the task of determining the correct meaning or sense of a wordin context. It is an intermediate task essential to many naturallanguage processing problems, including machine translation,information retrieval and speech processing. We focus on WSDin the context of machine translation. This involves resolvingambiguous words in the input sentence, so that words in a targetlanguage reflecting the correct senses can be chosen - a processalso known as target word selection. The WSD moduledeveloped here will be used to complement and improve anexisting example-based machine translation system. Ours will bea hybrid approach, drawing on a lexical ontology, which will bedeveloped as part of this research and a bilingual knowledge-baseof translated sentence pairs as the knowledge source. In addition,the lexical ontology to be constructed will be a reusable resourcefor other NLP tasks.KeywordsWord sense disambiguation, machine translation, target wordselection, lexical ontology.1. INTRODUCTIONWord sense disambiguation (WSD) refers to the task ofdetermining the correct meaning or sense of a word in context[7]. Approaches to WSD have moved from AI methods in the1960's, to knowledge-based methods in the 1980's, which firstused knowledge drawn from dictionaries and thesauri. Since the1990's, statistical and corpus-based methods, which first existedin the late 19th century[4], have regained a strong popularity.Rather than an isolated problem, WSD is an intermediate taskwhich often forms part of other natural language processing(NLP) tasks, such as machine translation, information retrievaland speech synthesis. In particular, this research will focus onWSD in the context of machine translation.2. RESEARCH MOTIVATIONMachine Translation (MT), or automatic translation, is the"application of computers to the translation of texts from onenatural language into another" [3J. Given an input sentence in asource language (SL), an MT system must produce an outputsentence in a target language (TL) that is both structurally andsemantically correct. To fulfil the latter requirement, ambiguousCopyright is held by the author.USM Computer Science Postgraduate Colloquium 2004,25-26th May 2004,Penang, Malaysia.Tang Enya KongComputer Aided Translation UnitSchool of Computer Sciences UniversitiSains Malaysia11800 Minden, Penang, Malaysia+60(0) 4 6533888 ext 4386enyakong@cs.usm.mywords in the input sentence must be disambiguated, so that wordsin the TL reflecting the correct senses can be chosen - a processalso known as target word selectiol1Following the limited success ofready availability of corpora resources, corpus-based methods forMT using parallel texts have become increasingly popular inrecent years. Example-based Machine Translation (EBMT) usesexisting translations, i.e. aligned pairs of sentences in the sourcelanguage and target language, as a basis to translate new inputsentences.AI-Adhaileh and Tang's EBMT system [1] compares thedependency structure of an input sentence with those oftranslation examples in a Bilingual Knowledge Bank (BKB).This, in addition to part-of-speech information, solves the WSDand word selection problem to a certain extent - but stillinsufficient to produce satisfactory translation frequently enough.Hence, extending this system with a WSD module shouldimprove the quality of the translation results. We propose ahybrid model, using the corpus in the said BKB (corpus-based)and a lexical ontology (knowledge-base'd) as our knowledgesource.3. LEXICAL ONTOLOGYAn ontology is an "explicit formal specifications of the terms inthe domain and relations among them" [2]. It defines concepts,terms and vocabularies in a domain, and also the relationshipamong these concepts. Concepts are organized in a taxonomy,with sub-classes inheriting properties and specializing fromsuper-classes. This similarity to the object-oriented paradigmserve to provide a knowledge representation that is naturallyunderstood by humans while being machine- interpretable.Ontologies are used to share common understanding of thestructure of information, to enable reuse of domain knowledge,and to separate domain knowledge from operational knowledge[8]. Ontologies form a knowledge base together with instancescreated from defmed classes. They can be a powerful tool fortasks such as translation, where information authored in a singlelanguage is converted for multiple target platforms, or forspecification and development of software [5].We propose the construction of a lexical ontology, where eachlexical entry of a word's senses will contain a variety oflinguistic and semantic information. We will use thisinformation, together with the hierarchy and relationships in theontology, to disambiguate an ambiguous input word, in order toselect an appropriate translation word. In addition, theinformation contained in the ontology can also be reused forI Direct approach (19608), interlingua. approach (1970s, and then late1980s to early 19908), and transfer approach (70s, 80s and 90s).other NLP tasks, such as information retrieval and speechprocessing.4. SUMMARYWe propose the construction of a lexical ontology as part of anapproach to WSD in machine translation. Although many otherresearchers (e.g. [6] and [7]) have adopted similar methods, wewill construct our ontology to suit an existing Example-BasedMachine Translation system.5. REFERENCES[1] AI-Adhaileh, M. H. and Tang, E.K. (1999). Example-BasedMachine Translation Based on the Synchronous SSTCAnnotation Schema. In Proceedings ofMachine TranslationSummit VII, Singapore, pp 244-249.[2] Gruber, T. (1993). A Translation Approach to PortableOntology Specification. Knowledge Acquisition, 5(2), pp.199-220.[3] Hutchins, W,J. (1986). Machine Translation: Past, Present,Future. Ellis Horwood, Chichester, UK.[4] Ide, N. and Veronis, J. (1998). Word Sense Disambiguation:The State of the Art. Computational Linguistics, 24(1), pp 1-41.[5] Jasper, R. and Uschold, M. (1999). A Framework forUnderstanding and ClassifYing Ontology Applications. InProceedings ofthe 11'/ BanffKnowledge Acquisition forKnowledge Based Systems Workshop, University ofCalgary/Stanford University.[6] Kang, S. 1., Lee, 1. H. (2001). Ontology-Based Word SenseDisambiguation by Using Semi-Automatically ConstructedOntology. In Proceedings ofMT Summit VIII, Santialgo deCompostela, Galicia, Spain.[7] Ng, H. T., Wang, B., Chan, Y. S. (2003). Exploiting ParallelTexts for Word Sense Disambiguation: An Empirical Study.In Proceedings ofthe 4r Annual Meeting oftheAssociation for Computational Linguistics, SapporoConvention Center, Sapporo, Japan, pp. 455-462.[8] Noy, N. F. and McGuinness, D. L. (2001). OntologyDevelopment 101: A Guide to Creating Your FirstOntology. Stanford Knowledge Systems LaboratoryTechnical Report KSL-OI-05 and Stanford MedicalInformatics Technical Report SMI-200I·0880, StanfordUniversity, Stanford.

A Framework for Understanding and ClassifYing Ontology Applications.

A Translation Approach to Portable Ontology Specification.

Example-Based Machine Translation Based on the Synchronous SSTC Annotation Schema.

Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study.

Ontology Development 101: A Guide to Creating Your First Ontology. StanfordKnowledge Systems Laboratory

Ontology-Based Word Sense Disambiguation by Using Semi-Automatically Constructed Ontology.

Word Sense Disambiguation: The State ofthe Art.

Lost in Translation: Word Sense Disambiguation.

http://eprints.usm.my/7811/1/Lost_in_Translation.pdf

Lost in Translation: Word Sense Disambiguation.

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