Search (5 results, page 1 of 1)

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Abstract

This paper describes exploitation of semantic annotations in the design and architecture of MIaS (Math Indexer and Searcher) system for mathematics retrieval. Basing on the claim that navigational and research search are 'killer' applications for digital library such as the European Digital Mathematics Library, EuDML, we argue for an approach based on Natural Language Processing techniques as used in corpus management systems such as the Sketch Engine, that will reach web scalability and avoid inference problems. The main ideas are 1) to augment surface texts (including math formulae) with additional linked representations bearing semantic information (expanded formulae as text, canonicalized text and subformulae) for indexing, including support for indexing structural information (expressed as Content MathML or other tree structures) and 2) use semantic user preferences to order found documents. The semantic enhancements of the MIaS system are being implemented as a math-aware search engine based on the state-of-the-art system Apache Lucene, with support for [MathML] tree indexing. Scalability issues have been checked against more than 400,000 arXiv documents.

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Abstract

Large corpora are ubiquitous in today's world and memory quickly becomes the limiting factor in practical applications of the Vector Space Model (VSM). In this paper, we identify a gap in existing implementations of many of the popular algorithms, which is their scalability and ease of use. We describe a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion. Within this framework, we implement several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation, in a way that makes them completely independent of the training corpus size. Particular emphasis is placed on straightforward and intuitive framework design, so that modifications and extensions of the methods and/or their application by interested practitioners are effortless. We demonstrate the usefulness of our approach on a real-world scenario of computing document similarities within an existing digital library DML-CZ.

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Abstract

A math-aware, full-text indexing based search engine that enables users to search for mathematical formulae inside documents. Search engine is unique because it is able to index and search structural information like representation of mathematical formulae. There is no other software or IR system that is able to store three billions of formulae in its index and search it with response time below a second. MIaS processes documents containing mathematical notation in MathML format. The system is built as an extension to any full-text indexing engine and has been verifiend on state-of-the-art Lucene core. It is scalable - it was verified to index almost whole arxiv.org (440,000 papers) having more than 160,000,000 formulae. Software is being used in EuDML (eudml.org) and other digital libraries. For more details see papers in peer reviewed conferences: [1] Sojka, Petr; Líska, Martin. In Matthew R. B. Hardy, Frank Wm. Tompa. Proceedings of the 2011 ACM Symposium on Document Engineering. Mountain View, CA, USA : ACM, 2011. pp.57--60. [2] Sojka, Petr; Líska, Martin. In J.H.Davenport, W.M. Farmer, J.Urban, F. Rabe. Intelligent Computer Mathematics LNCS 6824. Springer, 2011, pp.228--243.

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Abstract

In this document we describe the final release of the toolset for entity and semantic associations, integrating two versions (language dependent and language independent) of Unsupervised Document Similarity implemented by MU (using gensim tool) and Citation Indexing, Resolution and Matching (UJF/CMD). We give a brief description of tools, the rationale behind decisions made, and provide elementary evaluation. Tools are integrated in the main project result, EuDML website, and they deliver the needed functionality for exploratory searching and browsing the collected documents. EuDML users and content providers thus benefit from millions of algorithmically generated similarity and citation links, developed using state of the art machine learning and matching methods.

Content

Vgl. auch: https://is.muni.cz/repo/1076213/en/Lee-Sojka-Rehurek-Bolikowski/Toolset-for-Entity-and-Semantic-Associations-Initial-Release-Deliverable-82-of-project-EuDML?lang=en.

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Content

Vgl.: DocEng2011, September 19-22, 2011, Mountain View, California, USA Copyright 2011 ACM 978-1-4503-0863-2/11/09

Date

22. 2.2017 13:00:42