TAILIEUCHUNG - Báo cáo khoa học: "Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature"

We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information. We use a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We performed experiments on extracting gene and protein interactions from two different data sets. The results show that our approach outperforms most of the previous methods based on syntactic and semantic information. . | Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature Claudio Giuliano and Alberto Lavelli and Lorenza Romano ITC-irst Via Sommarive 18 38050 Povo TN Italy giuliano lavelli romano @ Abstract We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information. We use a combination of kernel functions to integrate two different information sources i the whole sentence where the relation appears and ii the local contexts around the interacting entities. We performed experiments on extracting gene and protein interactions from two different data sets. The results show that our approach outperforms most of the previous methods based on syntactic and semantic information. 1 Introduction Information Extraction IE is the process of finding relevant entities and their relationships within textual documents. Applications of IE range from Semantic Web to Bioinformatics. For example there is an increasing interest in automatically extracting relevant information from biomedical literature. Recent evaluation campaigns on bio-entity recognition such as BioCreAtIvE and JNLPBA 2004 shared task have shown that several systems are able to achieve good performance even if it is a bit worse than that reported on news articles . However relation identification is more useful from an applicative perspective but it is still a considerable challenge for automatic tools. In this work we propose a supervised machine learning approach to relation extraction which is applicable even when deep linguistic processing is not available or reliable. In particular we explore a kernel-based approach based solely on shallow linguistic processing such as tokeniza- tion sentence splitting Part-of-Speech PoS tagging and lemmatization. Kernel methods Shawe-Taylor and Cristianini 2004 show their full potential when an explicit computation of the feature map becomes computationally infeasible

• Báo cáo khoa học
• Exploiting Shallow Linguistic Information
• Relation Extraction
• Biomedical Literature
• 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