TWIRL: Translation with rule learning

Date of Publication

2005

Document Type

Bachelor's Thesis

Degree Name

Bachelor of Science in Computer Science

Subject Categories

Computer Sciences

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Michelle Wendy Tan

Defense Panel Member


Nathalie Lim

Charibeth K. Cheng

Abstract/Summary

Machine translation (MT) is the automatic conversion of a source language to a target language using computers. The two most common paradigms for machine translation are the rule based and example based approaches. The problem with the example based approach is that it needs to be domain specific and a large database of examples is needed to produce accurate translation results. Rule based approaches are known to produce high quality translations however, a linguist is necessary in deriving the set of rules. To overcome the problems of both the example based and the pure rule based paradigms, TWiRL used the rule based approach with an integration of machine learning of rules to allow flexibility in translation. Since no rule learning has been explored in English to Filipino machine translation system, the focus of this research is on translating English to Filipino text.

Keywords: Rule generalization, Compositionality, Bilingual Corpus, rule based, Machine Translation, Seed Rule Generation.

Abstract Format

html

Language

English

Format

Print

Accession Number

TU13621

Shelf Location

Archives, The Learning Commons, 12F, Henry Sy Sr. Hall

Physical Description

1 v. (various foliations) : ill. (some col.) ; 28 cm.

Keywords

Machine translating; Machine learning; Computational linguistics; Translating and interpreting; Artificial intelligence; Application software

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