Show simple item record

AuthorElloumi, Samir
AuthorJaoua, Ali
AuthorFerjani, Fethi
AuthorSemmar, Nasredine
AuthorBesançon, Romaric
AuthorAl-Jaam, Jihad
AuthorHammami, Helmi
Available date2024-03-20T01:55:07Z
Publication Date2013
Publication NameJournal of Information Science
ResourceScopus
ISSN1655515
URIhttp://dx.doi.org/10.1177/0165551512464140
URIhttp://hdl.handle.net/10576/53260
AbstractStarting from an ontology of a targeted financial domain corresponding to transaction, performance and management change news, relevant segments of text containing at least a domain keyword are extracted. The linguistic pattern of each segment is automatically generated to serve initially as a learning model. Each pattern is composed of named entities, keywords and articulation words. Some generic named entities like organizations, persons, locations, dates and grammatical annotations are generated by an automatic tool. During the learning step, each relevant segment is manually annotated with respect to the targeted entities (roles) structuring an event of the ontology. Information extraction is processed by associating a role with a specific entity. By alignment of generic entities to specific entities, some strings of a text are automatically annotated. An original learning approach is presented. Experiments with the management change event showed how recognition rates are improved by using different generalization tools.
SponsorThis publication was made possible by a grant from the Qatar National Research Fund NPRP 08-583-1-101. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the QNRF. Also, the authors would like to thank Dr Christopher J. Leonard for proofreading the paper.
Languageen
PublisherSAGE Publications Ltd
Subjectautomatic pattern generation
generic model
information extraction levels
management change event
named entities
TitleGeneral learning approach for event extraction: Case of management change event
TypeArticle
Pagination211-224
Issue Number2
Volume Number39
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record