TAILIEUCHUNG - Báo cáo khoa học: "Predicting Strong Associations on the Basis of Corpus Data"

Current approaches to the prediction of associations rely on just one type of information, generally taking the form of either word space models or collocation measures. At the moment, it is an open question how these approaches compare to one another. In this paper, we will investigate the performance of these two types of models and that of a new approach based on compounding. The best single predictor is the log-likelihood ratio, followed closely by the document-based word space model. We will show, however, that an ensemble method that combines these two best approaches with the compounding algorithm achieves. | Predicting Strong Associations on the Basis of Corpus Data Yves Peirsman Dirk Geeraerts Research Foundation - Flanders QLVL University of Leuven QLVL University of Leuven Leuven Belgium Leuven Belgium Abstract Current approaches to the prediction of associations rely on just one type of information generally taking the form of either word space models or collocation measures. At the moment it is an open question how these approaches compare to one another. In this paper we will investigate the performance of these two types of models and that of a new approach based on compounding. The best single predictor is the log-likelihood ratio followed closely by the document-based word space model. We will show however that an ensemble method that combines these two best approaches with the compounding algorithm achieves an increase in performance of almost 30 over the current state of the art. 1 Introduction Associations are words that immediately come to mind when people hear or read a given cue word. For instance a word like pepper calls up salt and wave calls up sea. Aitchinson 2003 and Schulte im Walde and Melinger 2005 show that such associations can be motivated by a number of factors from semantic similarity to collocation. Current computational models of association however tend to focus on one of these by using either collocation measures Michelbacher et al. 2007 or word space models Sahlgren 2006 Peirsman et al. 2008 . To this day two general problems remain. First the literature lacks a comprehensive comparison between these general types of models. Second we are still looking for an approach that combines several sources of information so as to correctly predict a larger variety of associations. Most computational models of semantic relations aim to model semantic similarity in particu lar Landauer and Dumais 1997 Lin 1998 Pado and Lapata 2007 . In Natural Language Processing these models have .

• Báo cáo khoa học
• Predicting Strong Associations
• the Basis of Corpus Data
• Dirk Geeraerts Yves
• 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