ScaleText: The Design of a Scalable, Adaptable and User-Friendly Document System for Similarity Searches : Digging for Nuggets of Wisdom in Text

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Authors

RYGL Jan SOJKA Petr RŮŽIČKA Michal ŘEHŮŘEK Radim

Type Article in Proceedings
Conference Proceedings of the Tenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2016
MU Faculty or unit

Faculty of Informatics

Citation
WWW
Field Informatics
Keywords ScaleText; vector space modelling; Latent Semantic Indexing; LSI; machine learning; scalable search; search system design; text mining
Description This paper describes the design of a new ScaleText system aimed at scalable semantic indexing of heterogeneous textual corpora. We discuss the design decisions that lead to a modular system architecture for indexing and searching using semantic vectors of document segments – nuggets of wisdom. The prototype system implementation is evaluated by applying Latent Semantic Indexing (LSI) on the Enron corpus. And the Bpref measure is used to automate comparing the performance of different algorithms and system configurations.
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