tailieunhanh - Báo cáo khoa học: "Bootstrapping Named Entity Recognition with Automatically Generated Gazetteer Lists"

Current Named Entity Recognition systems suffer from the lack of hand-tagged data as well as degradation when moving to other domain. This paper explores two aspects: the automatic generation of gazetteer lists from unlabeled data; and the building of a Named Entity Recognition system with labeled and unlabeled data. | Bootstrapping Named Entity Recognition with Automatically Generated Gazetteer Lists Zornitsa Kozareva Dept. de Lenguajes y Sistemas Informaticos University of Alicante Alicante Spain zkozareva@ Abstract Current Named Entity Recognition systems suffer from the lack of hand-tagged data as well as degradation when moving to other domain. This paper explores two aspects the automatic generation of gazetteer lists from unlabeled data and the building of a Named Entity Recognition system with labeled and unlabeled data. 1 Introduction Automatic information extraction and information retrieval concerning particular person location organization title of movie or book juxtaposes to the Named Entity Recognition NER task. NER consists in detecting the most silent and informative elements in a text such as names of people company names location monetary currencies dates. Early NER systems Fisher et al. 1997 Black et al. 1998 etc. participating in Message Understanding Conferences MUC used linguistic tools and gazetteer lists. However these are difficult to develop and domain sensitive. To surmount these obstacles application of machine learning approaches to NER became a research subject. Various state-of-the-art machine learning algorithms such as Maximum Entropy Borthwick 1999 AdaBoost Carreras et al. 2002 Hidden Markov Models Bikel et al. Memory-based Based learning Tjong Kim Sang 2002b have been used1. Klein et al. 2003 Mayfield et al. 2003 Wu et al. 2003 Kozareva et al. 2005c among others combined several classifiers to obtain better named entity coverage rate. 1For other machine learning methods consult http conll2002 ner http conll2003 ner Nevertheless all these machine learning algorithms rely on previously hand-labeled training data. Obtaining such data is labor-intensive time consuming and even might not be present for languages with limited funding. Resource limitation directed NER research Collins and Singer 1999 .

• Báo cáo khoa học
• Bootstrapping Named Entity Recognition
• Automatically Generated Gazetteer Lists
• Zornitsa Kozareva
• 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