TAILIEUCHUNG - Báo cáo khoa học: " Linking Events and Relations to Timestamps"

We present work on linking events and fluents (., relations that hold for certain periods of time) to temporal information in text, which is an important enabler for many applications such as timelines and reasoning. Previous research has mainly focused on temporal links for events, and we extend that work to include fluents as well, presenting a common methodology for linking both events and relations to timestamps within the same sentence. Our approach combines tree kernels with classical feature-based learning to exploit context and achieves competitive F1-scores on event-time linking, and comparable F1scores for fluents. Our best systems achieve. | When Did that Happen Linking Events and Relations to Timestamps Dirk Hovy James Fan Alfio Gliozzo Siddharth Patwardhan and Chris Welty IBM T. J. Watson Research Center 19 Skyline Drive Hawthorne NY 10532 dirkh@ fanj gliozzo siddharth welty @ Abstract We present work on linking events and fluents . relations that hold for certain periods of time to temporal information in text which is an important enabler for many applications such as timelines and reasoning. Previous research has mainly focused on temporal links for events and we extend that work to include fluents as well presenting a common methodology for linking both events and relations to timestamps within the same sentence. Our approach combines tree kernels with classical feature-based learning to exploit context and achieves competitive F1-scores on event-time linking and comparable F1-scores for fluents. Our best systems achieve F1-scores of on events and on fluents. 1 Introduction It is a long-standing goal of NLP to process natural language content in such a way that machines can effectively reason over the entities relations and events discussed within that content. The applications of such technology are numerous including intelligence gathering business analytics healthcare education etc. Indeed the promise of machine reading is actively driving research in this area Etzioni et al. 2007 Barker et al. 2007 Clark and Harrison 2010 Strassel et al. 2010 . Temporal information is a crucial aspect of this task. For a machine to successfully understand natural language text it must be able to associate time points and temporal durations with relations and events it discovers in text. The first author conducted this research during an internship at IBM Research. In this paper we present methods to establish links between events . bombing or election or fluents . spouseOf or em-ployedBy and temporal expressions . last Tuesday and November 2008 . While previous research

• Báo cáo khoa học
• Linking Events
• Relations to Timestamps
• Dirk Hovy
• 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