TAILIEUCHUNG - Báo cáo khoa học: "Weakly Supervised Approaches for Ontology Population"

We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsupervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories - geographical locations and person names and ten sub-classes for each category. For each sub-class, from a list of training examples and a syntactically parsed corpus, we automatically learn a syntactic model - a set of weighted syntactic features, . words which typically co-occur in certain syntactic positions with the members of that class. . | Weakly Supervised Approaches for Ontology Population Hristo Tanev Tanev ITC-irst 38050 Povo Trento Italy htanev@ Bernardo Magnini ITC-irst 38050 Povo Trento Italy magnini@ Abstract We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsupervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories - geographical locations and person names and ten sub-classes for each category. For each sub-class from a list of training examples and a syntactically parsed corpus we automatically learn a syntactic model - a set of weighted syntactic features . words which typically co-occur in certain syntactic positions with the members of that class. The model is then used to classify the unknown Named Entities in the test set. The method is weakly supervised since no annotated corpus is used in the learning process. We achieved promising results . 65 accuracy outperforming significantly previous unsupervised approaches. 1 Introduction Automatic Ontology Population OP from texts has recently emerged as a new field of application for knowledge acquisition techniques see among others Buitelaar et al. 2005 . Although there is no a univocally accepted definition for the OP task a useful approximation has been suggested Bontcheva and Cunningham 2003 as Ontology Driven Information Extraction where in place of a template to be filled the goal of the task is the extraction and classification of instances of concepts and relations defined in a Ontology. The task has been approached in a variety of similar perspectives including term clustering . Lin 1998a and Almuhareb and Poesio 2004 and term categorization . Avancini et al. 2003 . A rather different task is Ontology Learning OL where new concepts and relations are supposed to be acquired with the consequence of changing the definition of the Ontology itself see for instance .

• Báo cáo khoa học
• Weakly Supervised Approaches
• Ontology Population
• Hristo Tanev
• 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