%0 Conference Proceedings
%T A Knowledge Extraction and Management Component to Support Spontaneous Participation
%+ National University of Ireland [Galway] (NUI Galway)
%A Porwol, Lukasz
%A Hassan, Islam
%A Ojo, Adegbojega
%A Breslin, John
%Z Part 2: Deliberation and Consultation
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 7th International Conference on Electronic Participation (ePart)
%C Thessaloniki, Greece
%3 Electronic Participation
%V LNCS-9249
%P 68-80
%8 2015-08-30
%D 2015
%R 10.1007/978-3-319-22500-5_6
%K e-Participation
%K Citizen-led e-Participation
%K Information extraction (IE)
%K Natural Language Processing (NLP)
%K Public services
%K e-Government
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X Harnessing spontaneous contributions of citizens on Social Media and networking sites is a major feature of the next generation citizen-led e-Participation paradigm. However, extracting information of interest from Social Media streams is a challenging task and requires support from domain specific language resources such as lexica. This work describes our efforts at developing a Knowledge Extraction and Management component which employs a lexicon for extracting information related to public services in Social Media contents or streams as part of a holistic technology infrastructure for citizen-led e-Participation. Our approach consists of three basic steps – (1) acquisition and refinement of public service catalogues, (2) organization of the public service names into a lexicon based on different semantic similarity measures and (3) development of a dictionary-based Named Entity Recognizer (NER) or “spotter” based on the lexicon. We evaluate the performance of the NER solution supported by contextual information generated by two well-known general-purpose information NER tools (DBpedia Spotlight and Alchemy) on a dataset of tweets. Results show that our strategy to domain specific information extraction from Social Media is effective. We conclude with a scenario on how our approach could be scaled-up to extract other types of information from citizen discussions on Social Media.
%G English
%Z TC 8
%Z WG 8.5
%2 https://inria.hal.science/hal-01587630/document
%2 https://inria.hal.science/hal-01587630/file/346775_1_En_6_Chapter.pdf
%L hal-01587630
%U https://inria.hal.science/hal-01587630
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-EPART
%~ IFIP-WG8-5
%~ IFIP-LNCS-9249