tailieunhanh - Báo cáo khoa học: "Combining Distributional and Morphological Information for Part of Speech Induction"

In this paper we discuss algorithms for clustering words into classes from unlabelled text using unsupervised algorithms, based on distributional and morphological information. We show how the use of morphological information can improve the performance on rare words, and that this is robust across a wide range of languages. | Combining Distributional and Morphological Information for Part of Speech Induction Alexander Clark ISSCO TIM University of Geneva UNI-MAIL Boulevard du Pont-d Arve CH-1211 Geneve 4 Switzerland Abstract In this paper we discuss algorithms for clustering words into classes from unlabelled text using unsupervised algorithms based on distributional and morphological information. We show how the use of morphological information can improve the performance on rare words and that this is robust across a wide range of languages. 1 Introduction The task studied in this paper is the unsupervised learning of parts-of-speech that is to say lexical categories corresponding to traditional notions of for example nouns and verbs. As is often the case in machine learning of natural language there are two parallel motivations first a simple engineering one - the induction of these categories can help in smoothing and generalising other models particularly in language modelling for speech recognition as explored by Ney et al. 1994 and secondly a cognitive science motivation - exploring how evidence in the primary linguistic data can account for first language acquisition by infant children Finch and Chater 1992a Finch and Chater 1992b Redington et al. 1998 . At this early phase of learning only limited sources of information can be used primarily distributional evidence about the contexts in which words occur and morphological evidence more strictly phonotactic or orthotactic evidence about the sequence of symbols letters or phonemes of which each word is formed. A number of different approaches have been presented for this task using exclusively distributional evidence to cluster the words together starting with Lamb 1961 and these have been shown to produce good results in English Japanese and Chinese. These languages have however rather simple morphology and thus words will tend to have higher frequency than in more morphologically complex languages. In

• Báo cáo khoa học
• Combining Distributional
• Morphological Information
• Part of Speech Induction
• 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