tailieunhanh - Báo cáo khoa học: "A Word-to-Word Model of Translational Equivalence"

Many multilingual NLP applications need to translate words between different languages, but cannot afford the computational expense of inducing or applying a full translation model. For these applications, we have designed a fast algorithm for estimating a partial translation model, which accounts for translational equivalence only at the word level . The model's precision/recall trade-off can be directly controlled via one threshold parameter. This feature makes the model more suitable for applications that are not fully statistical. The model's hidden parameters can be easily conditioned on information extrinsic to the model, providing an easy way to integrate pre-existing. | A Word-to-Word Model of Translational Equivalence I. Dan Melamed Dept of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 . Abstract Many multilingual NLP applications need to translate words between different languages but cannot afford the computational expense of inducing or applying a full translation model. For these applications we have designed a fast algorithm for estimating a partial translation model which accounts for translational equivalence only at the word level . The model s preci-sion recall trade-off can be directly controlled via one threshold parameter. This feature makes the model more suitable for applications that are not fully statistical. The model s hidden parameters can be easily conditioned on information extrinsic to the model providing an easy way to integrate pre-existing knowledge such as part-of-speech dictionaries word order etc. Our model can link word tokens in parallel texts as well as other translation models in the literature. Unlike other translation models it can automatically produce dictionary-sized translation lexicons and it can do so with over 99 accuracy. 1 Introduction Over the past decade researchers at IBM have developed a series of increasingly sophisticated statistical models for machine translation Brown et al. 1988 Brown et al. 1990 Brown et al. 1993a . However the IBM models which attempt to capture a broad range of translation phenomena are computationally expensive to apply. Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications including crummy MT on the World Wide Web Church Hovy 1993 certain machine-assisted translation tools . Macklovitch 1994 Melamed 1996b concordancing for bilingual lexicography Catizone et al. 1993 Gale Church 1991 computer-assisted language learning corpus linguistics Melby. 1981 and cross-lingual information retrieval Card Dorr 1996 . In this paper we

• Báo cáo khoa học
• A Word to Word Model
• Translational Equivalence
• Workshops
• 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
• 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
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln