General learning approach for event extraction: Case of management change event

Samir Elloumi, Ali Jaoua, Fethi Ferjani, Nasredine Semmar, Romaric Besancon (), Jihad Al-Jaam and Helmi Hammami
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Samir Elloumi: Computer Science and Engineering Department [Doha] (University of Qatar, College of Engineering)
Ali Jaoua: Computer Science and Engineering Department [Doha] (University of Qatar, College of Engineering)
Fethi Ferjani: Computer Science and Engineering Department [Doha] (University of Qatar, College of Engineering)
Nasredine Semmar: DIASI (CEA, LIST) - Département Intelligence Ambiante et Systèmes Interactifs - LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies - DRT (CEA) - Direction de Recherche Technologique (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay
Romaric Besancon: DIASI (CEA, LIST) - Département Intelligence Ambiante et Systèmes Interactifs - LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies - DRT (CEA) - Direction de Recherche Technologique (CEA) - CEA - Commissariat à l'énergie atomique et aux énergies alternatives - Université Paris-Saclay
Jihad Al-Jaam: Computer Science and Engineering Department [Doha] (University of Qatar, College of Engineering)
Helmi Hammami: College of Business and Economics, Qatar University - Qatar University

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Abstract: Starting 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.

Keywords: automatic pattern generation; generic model; information extraction levels; management change event; named entities (search for similar items in EconPapers)
Date: 2013-10-15
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Published in Journal of Information Science, 2013, 39, pp.211 - 224. ⟨10.1177/0165551512464140⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:cea-01777948

DOI: 10.1177/0165551512464140

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