Automatically Extracting Procedural Knowledge from Instructional Texts using Natural Language Processing

Ziqi Zhang, Phillip Webster, Victoria Uren, Andrea Varga, Ciravegna Fabio

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Procedural knowledge is the knowledge required to perform certain tasks, and forms an important part of expertise. A major source of procedural knowledge is natural language instructions. While these readable instructions have been useful learning resources for human, they are not interpretable by machines. Automatically acquiring procedural knowledge in machine interpretable formats from instructions has become an increasingly popular research topic due to their potential applications in process automation. However, it has been insufficiently addressed. This paper presents an approach and an implemented system to assist users to automatically acquire procedural knowledge in structured forms from instructions. We introduce a generic semantic representation of procedures for analysing instructions, using which natural language techniques are applied to automatically extract structured procedures from instructions. The method is evaluated in three domains to justify the generality of the proposed semantic representation as well as the effectiveness of the implemented automatic system.
Original languageEnglish
Title of host publicationInternational Conference on Language Resources and Evaluation (LREC 2012)
EditorsNicoletta Calzolari, Khalid Choukri, Thierry Declerck, Mehmet U. Doğan, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis
Pages520-527
Number of pages8
Publication statusPublished - 2012
Event8th International Conference on Language Resources and Evaluation - Istanbul, Turkey
Duration: 21 May 201227 May 2012

Conference

Conference8th International Conference on Language Resources and Evaluation
Abbreviated titleLREC 2012
Country/TerritoryTurkey
CityIstanbul
Period21/05/1227/05/12

Keywords

  • procedural knowledge
  • information extraction
  • instructional text

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