TAILIEUCHUNG - Báo cáo khoa học: "Weakly Supervised Part-of-Speech Tagging for Morphologically-Rich, Resource-Scarce Languages"

This paper examines unsupervised approaches to part-of-speech (POS) tagging for morphologically-rich, resource-scarce languages, with an emphasis on Goldwater and Griffiths’s (2007) fully-Bayesian approach originally developed for English POS tagging. We argue that existing unsupervised POS taggers unrealistically assume as input a perfect POS lexicon, and consequently, we propose a weakly supervised fully-Bayesian approach to POS tagging, which relaxes the unrealistic assumption by automatically acquiring the lexicon from a small amount of POS-tagged data | Weakly Supervised Part-of-Speech Tagging for Morphologically-Rich Resource-Scarce Languages Kazi Saidul Hasan and Vincent Ng Human Language Technology Research Institute University of Texas at Dallas Richardson TX 75083-0688 saidul vince @ Abstract This paper examines unsupervised approaches to part-of-speech POS tagging for morphologically-rich resource-scarce languages with an emphasis on Goldwater and Griffiths s 2007 Pully-Bayesian approach originally developed for English POS tagging. We argue that existing unsupervised POS taggers unrealistically assume as input a perfect POS lexicon and consequently we propose a weakly supervised fully-Bayesian approach to POS tagging which relaxes the unrealistic assumption by automatically acquiring the lexicon from a small amount of POS-tagged data. Since such relaxation comes at the expense of a drop in tagging accuracy we propose two extensions to the Bayesian framework and demonstrate that they are effective in improving a fully-Bayesian POS tagger for Bengali our representative morphologically-rich resource-scarce language. 1 Introduction Unsupervised POS tagging requires neither manual encoding of tagging heuristics nor the availability of data labeled with POS information. Rather an unsupervised POS tagger operates by only assuming as input a POS lexicon which consists of a list of possible POS tags for each word. As we can see from the partial POS lexicon for English in Figure 1 the is unambiguous with respect to POS tagging since it can only be a determiner DT whereas sting is ambiguous since it can be a common noun NN a proper noun NNP or a verb VB . In other words the lexicon imposes constraints on the possible POS tags Word POS tag s running NN JJ sting NN NNP VB the DT Figure 1 A partial lexicon for English of each word and such constraints are then used by an unsupervised tagger to label a new sentence. Conceivably tagging accuracy decreases with the increase in ambiguity unambiguous words .

• Báo cáo khoa học
• Weakly Supervised Part of Speech Tagging
• 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