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                <forename type="first">Lukasz</forename>
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            <idno type="halRefHtml">&lt;i&gt;7th International Conference on Electronic Participation (ePart)&lt;/i&gt;, Aug 2015, Thessaloniki, Greece. pp.68-80, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-319-22500-5_6"&gt;&amp;#x27E8;10.1007/978-3-319-22500-5_6&amp;#x27E9;&lt;/a&gt;</idno>
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                    <forename type="first">Lukasz</forename>
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                  <title>7th International Conference on Electronic Participation (ePart)</title>
                  <date type="start">2015-08-30</date>
                  <date type="end">2015-09-02</date>
                  <settlement>Thessaloniki</settlement>
                  <country key="GR">Greece</country>
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                <term xml:lang="en">e-Government</term>
                <term xml:lang="en">Public services</term>
                <term xml:lang="en">Natural Language Processing (NLP)</term>
                <term xml:lang="en">Information extraction (IE)</term>
                <term xml:lang="en">Citizen-led e-Participation</term>
                <term xml:lang="en">e-Participation</term>
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              <p>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.</p>
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          <orgName>National University of Ireland [Galway]</orgName>
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          <date type="start">1845-12-30</date>
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