Analogical learner for natural language processing based on structured string-tree correspondence(sstc)and case-based reasoning

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dc.contributor.authorHuan Ngee, Lim
dc.date.accessioned2015-08-25T07:43:00Z
dc.date.available2015-08-25T07:43:00Z
dc.date.issued2009-05
dc.description.abstractExample-Based Machine Translation (EBMT) is using the similar translation examples which are retrieved from the Bilingual Knowledge Bank (BKB) to translate an input sentence. The examples (source and target pairs) in the BKB are annotated based on a flexible annotation schema known as Synchronous Structured String- Tree Correspondence (S-SSTC). Indexing approach has been implemented into our current English-Malay EBMT to ensure fast retrieval of appropriate examples in the BKB for EBMT to produce well-formed translations. The source and target example pairs in the BKB are indexed in word and structure level. The structural indexes are classified according to different types and structures of examples. Analogy method is introduced to the EBMT system to increase the accuracy of translation. Using analogy method, we can identify more appropriate BKB examples for a given input sentence. From the examples, we derive as many templates as possible using analogy proportion. These templates are more structurally related to the input sentence compared to the structural indexes return by the current approach because the structural indexes are picked based on certain criteria fixed by the researcher. After the derivation of the templates, we construct its tree representations using case-based reasoning method. The purpose of constructing tree representations is to validate the templates which we have derived. Each template must correspond to its tree representation. We have made a comparison between analogy method and structural indexing approach in term of accuracy of translations and the evaluation results shown that our new approach achieves better results than existing approach.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1123
dc.language.isoenen_US
dc.subjectLearner for natural languageen_US
dc.subjectStructured string-tree correspondence(sstcen_US
dc.subjectCase-based reasoningen_US
dc.titleAnalogical learner for natural language processing based on structured string-tree correspondence(sstc)and case-based reasoningen_US
dc.typeThesisen_US
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