TAILIEUCHUNG - Báo cáo khoa học: "Bilingually Motivated Domain-Adapted Word Segmentation for Statistical Machine Translation"

We introduce a word segmentation approach to languages where word boundaries are not orthographically marked, with application to Phrase-Based Statistical Machine Translation (PB-SMT). Instead of using manually segmented monolingual domain-specific corpora to train segmenters, we make use of bilingual corpora and statistical word alignment techniques. First of all, our approach is adapted for the specific translation task at hand by taking the corresponding source (target) language into account. . | Bilingually Motivated Domain-Adapted Word Segmentation for Statistical Machine Translation Yanjun Ma Andy Way National Centre for Language Technology School of Computing Dublin City University Dublin 9 Ireland yma away @ Abstract We introduce a word segmentation approach to languages where word boundaries are not orthographically marked with application to Phrase-Based Statistical Machine Translation PB-SMT . Instead of using manually segmented monolingual domain-specific corpora to train segmenters we make use of bilingual corpora and statistical word alignment techniques. First of all our approach is adapted for the specific translation task at hand by taking the corresponding source target language into account. Secondly this approach does not rely on manually segmented training data so that it can be automatically adapted for different domains. We evaluate the performance of our segmentation approach on PB-SMT tasks from two domains and demonstrate that our approach scores consistently among the best results across different data conditions. 1 Introduction State-of-the-art Statistical Machine Translation SMT requires a certain amount of bilingual corpora as training data in order to achieve competitive results. The only assumption of most current statistical models Brown et al. 1993 Vogel et al. 1996 Deng and Byrne 2005 is that the aligned sentences in such corpora should be segmented into sequences of tokens that are meant to be words. Therefore for languages where word boundaries are not orthographically marked tools which segment a sentence into words are required. However this segmentation is normally performed as a preprocessing step using various word seg-menters. Moreover most of these segmenters are usually trained on a manually segmented domain specific corpus which is not adapted for the specific translation task at hand given that the manual segmentation is performed in a monolingual context. Consequently such segmenters cannot .

• Báo cáo khoa học
• Bilingually Motivated
• Domain Adapted Word Segmentation
• Statistical Machine Translation
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
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• UNGRAMHATICALITY
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• Taxonomy
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• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln