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A Lexical Resource for Identifying Public Services Names on the Social Web

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Social Media for Government Services

Abstract

Discovery of government-related resources on the social web through mentions of government-related terms requires domain-specific lexical resources. This chapter describes an approach for developing a Lexical Resource for Public Services Names and how it could be exploited. Central to our technical approach is the development of a Semantic Alignment Algorithm, which organizes a set of public service names automatically captured from government websites in a semantic network based on a semantic relatedness measure (Explicit Semantic Analysis—ESA). To demonstrate the use of the developed lexicon, we: (1) clustered the United Kingdom and Irish Government public services catalogue for easier access to related services on citizens portals and (2) developed a Named Entity Recognizer (NER) to identify mentions of public service related information in a twitter stream. Evaluation of the semantic relations in the developed lexical resource computed by our semantic alignment algorithm showed the accuracy (specifically the F-Score ranged from 0.65 to 0.93.

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Notes

  1. 1.

    https://joinup.ec.europa.eu/asset/core_public_service/description.

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  1. Insight Centre for Data Analytics, National University of Ireland Galway, IDA Business Park, Newcastle, Galway, Republic of Ireland

    Islam A. Hassan, Adegboyega Ojo & Lukasz Porwol

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  1. Islam A. Hassan
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  2. Adegboyega Ojo
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  2. Services Flagship, CSIRO Data61, Sydney, New South Wales, Australia

    Cécile Paris

  3. RMIT University, Melbourne, Victoria, Australia

    Dimitrios Georgakopoulos

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A. Hassan, I., Ojo, A., Porwol, L. (2015). A Lexical Resource for Identifying Public Services Names on the Social Web. In: Nepal, S., Paris, C., Georgakopoulos, D. (eds) Social Media for Government Services. Springer, Cham. https://doi.org/10.1007/978-3-319-27237-5_14

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