tailieunhanh - Báo cáo khoa học: "Cross-Lingual Genre Classification"

Classifying text genres across languages can bring the benefits of genre classification to the target language without the costs of manual annotation. This article introduces the first approach to this task, which exploits text features that can be considered stable genre predictors across languages. My experiments show this method to perform equally well or better than full text translation combined with monolingual classification, while requiring fewer resources. | Cross-Lingual Genre Classification Philipp Petrenz School of Informatics University of Edinburgh 10 Crichton Street Edinburgh Eh8 9AB uK Abstract Classifying text genres across languages can bring the benefits of genre classification to the target language without the costs of manual annotation. This article introduces the first approach to this task which exploits text features that can be considered stable genre predictors across languages. My experiments show this method to perform equally well or better than full text translation combined with monolingual classification while requiring fewer resources. 1 Introduction Automated text classification has become standard practice with applications in fields such as information retrieval and natural language processing. The most common basis for text classification is by topic Joachims 1998 Sebas-tiani 2002 but other classification criteria have evolved including sentiment Pang et al. 2002 authorship de Vel et al. 2001 Stamatatos et al. 2000a and author personality Oberlander and Nowson 2006 as well as categories relevant to filter algorithms . spam or inappropriate contents for minors . Genre is another text characteristic often described as orthogonal to topic. It has been shown by Biber 1988 and others after him that the genre of a text affects its formal properties. It is therefore possible to use cues . lexical syntactic structural from a text as features to predict its genre which can then feed into information retrieval applications Karlgren and Cutting 1994 Kessler et al. 1997 Finn and Kushmer-ick 2006 Freund et al. 2006 . This is because users may want documents that serve a particular communicative purpose as well as being on a particular topic. For example a web search on the topic crocodiles may return an encyclopedia entry a biological fact sheet a news report about attacks in Australia a blog post about a safari experience a fiction novel set in South Africa or a poem about

• Báo cáo khoa học
• Cross Lingual Genre Classification
• Philipp Petrenz
• Long Papers
• 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
• 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