Browsing by Author "Chang, JS"

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Bilingual sentence alignment based on punctuation statistics and lexicon
(2005)
Chuang, TC
;
Wu, JC
;
Lin, T
;
Shei, WC
;
Chang, JS
This paper presents a new method of aligning bilingual parallel texts based on punctuation statistics and lexical information, It is demonstrated that the punctuation statistics prove to be effective means to achieve good results. The task of sentence alignment of bilingual texts written in disparate language pairs like English and Chinese is reportedly more difficult. We examine the feasibility of using punctuations for high accuracy sentence alignment. Encouraging precision rate is demonstrated in aligning sentences in bilingual parallel corpora based solely on punctuation statistics. Improved results were obtained when both punctuation statistics and lexical information were employed. We have experimented with an implementation of the proposed method on the parallel corpora of Sinorama Magazine and Records of the Hong Kong Legislative Council with satisfactory results.
Extraction of name and transliteration in monolingual and parallel corpora
(2004)
Lin, T
;
Wu, JC
;
Chang, JS
;
電信工程研究所
;
Institute of Communications Engineering
Named-entities in free text represent a challenge to text analysis in Machine Translation and Cross Language Information Retrieval. These phrases are often transliterated into another language with a different sound inventory and writing system. Named-entities found in free text are often not listed in bilingual dictionaries. Although it is possible to identify and translate named-entities on the fly without a list of proper names and transliterations, an extensive list of existing transliterations certainly will ensure high precision rate. We use a seed list of proper names and transliterations to train a Machine Transliteration Model. With the model it is possible to extract proper names and their transliterations in monolingual or parallel corpora with high precision and recall rates.
Web-based unsupervised learning for query formulation in question answering
(2005)
Wang, YC
;
Wu, JC
;
Liang, T
;
Chang, JS
;
資訊工程學系
Converting questions to effective queries is crucial to open-domain question answering systems. In this paper, we present a web-based unsupervised learning approach for transforming a given natural-language question to an effective query. The method involves querying a search engine for Web passages that contain the answer to the question, extracting patterns that characterize fine-grained classification for answers, and linking these patterns with n-grams in answer passages. Independent evaluation on a set of questions shows that the proposed approach outperforms a naive keyword-based approach in terms of mean reciprocal rank and human effort.