TAILIEUCHUNG - Báo cáo khoa học: "Learning-Based Named Entity Recognition for Morphologically-Rich, Resource-Scarce Languages"

Named entity recognition for morphologically rich, case-insensitive languages, including the majority of semitic languages, Iranian languages, and Indian languages, is inherently more difficult than its English counterpart. Worse still, progress on machine learning approaches to named entity recognition for many of these languages is currently hampered by the scarcity of annotated data and the lack of an accurate part-of-speech tagger. | Learning-Based Named Entity Recognition for Morphologically-Rich Resource-Scarce Languages Kazi Saidul Hasan and Md. Altaf ur Rahman and Vincent Ng Human Language Technology Research Institute University of Texas at Dallas Richardson TX 75083-0688 saidul altaf vince @ Abstract Named entity recognition for morphologically rich case-insensitive languages including the majority of semitic languages Iranian languages and Indian languages is inherently more difficult than its English counterpart. Worse still progress on machine learning approaches to named entity recognition for many of these languages is currently hampered by the scarcity of annotated data and the lack of an accurate part-of-speech tagger. While it is possible to rely on manually-constructed gazetteers to combat data scarcity this gazetteer-centric approach has the potential weakness of creating irreproducible results since these name lists are not publicly available in general. Motivated in part by this concern we present a learning-based named entity recognizer that does not rely on manually-constructed gazetteers using Bengali as our representative resource-scarce morphologically-rich language. Our recognizer achieves a relative improvement of in F-measure over a baseline recognizer. Improvements arise from 1 using induced affixes 2 extracting information from online lexical databases and 3 jointly modeling part-of-speech tagging and named entity recognition. 1 Introduction While research in natural language processing has gained a lot of momentum in the past several decades much of this research effort has been focusing on only a handful of politically-important languages such as English Chinese and Arabic. On the other hand being the fifth most spoken lan-guage1 with more than 200 million native speakers residing mostly in Bangladesh and the Indian state of West Bengal Bengali has far less electronic resources than the aforementioned languages. In fact a major obstacle to the .

• Báo cáo khoa học
• Learning Based Named Entity Recognition
• Morphologically Rich
• Resource Scarce Languages
• 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
• 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