Abstract
Search engines are getting faster and more feature-rich year by year, striving to bring their users the information they need as fast as possible. Bringing relevant information to the user in an effortless manner is no easy task. The search feature set is where search engines compete to win their users and it usually describes in what manner a search engine may be different from others. One of the most challenging features in a search engine is to diversify the search results in a way that each result has different meaning or different content from others. The goal is to free the user from the burden of separating redundant results. What is redundant for the user is the key challenge of this feature and may have different meanings depending on the format of the information. For text searches, user’s input is commonly used for diversification of results [1]. This input may include information like the topic of search, previous searches or supplementary parameters asked by the engine. This paper describes a different approach on text diversification, based on text semantics analysis and combined with clustering algorithms. The aim is to explore how similarities from the semantic point of view can be used to eliminate redundant texts.
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- 1.
ROME Tools - http://rometools.github.io/rome/.
- 2.
Open Calais - How Does Calais Work? - http://www.opencalais.com/about.
- 3.
Apache Jena - https://jena.apache.org/.
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Acknowledgments
This work is supported by the PRIVATESKY project (Experimental development in public-private partnership for creating native Cloud platform with advanced features for data protection), from POC 2014-2020, Action 1.2.3, Partnerships for knowledge transfer.
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Micu, A., Iftene, A. (2016). Semantic Diversification of Text Search Results. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_8
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DOI: https://doi.org/10.1007/978-3-319-45246-3_8
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