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Combining Compositionality and Pagerank for the Identification of Semantic Relations between Biomedical Words
LIM&BIO (EA3969), Université Paris 13, France.
Mälardalen University, School of Education, Culture and Communication, Educational Sciences and Mathematics. (Mathematics and Applied Mathematics)ORCID iD: 0000-0002-1624-5147
Université Paris 13, France.
Universiteá Lille, Villeneuve d'Ascq, France.
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2012 (English)In: BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing, 2012, p. 109-117Conference paper, Published paper (Refereed)
Abstract [en]

The acquisition of semantic resources and relations is an important task for several applications, such as query expansion, information retrieval and extraction, machine translation. However, their validity should also be computed and indicated, especially for automatic systems and applications. We exploit the compositionality based methods for the acquisition of synonymy relations and of indicators of these synonyms. We then apply pagerank-derived algorithm to the obtained semantic graph in order to filter out the acquired synonyms. Evaluation performed with two independent experts indicates that the quality of synonyms is systematically improved by 10 to 15% after their filtering.

Place, publisher, year, edition, pages
2012. p. 109-117
National Category
Algebra and Logic Bioinformatics (Computational Biology) Other Computer and Information Science Language Technology (Computational Linguistics) Computational Mathematics Mathematics
Research subject
Mathematics/Applied Mathematics; Computer Science
Identifiers
URN: urn:nbn:se:mdh:diva-17938ISBN: 978-1-937284-20-6 (print)ISBN: 1-937284-20-4 (print)OAI: oai:DiVA.org:mdh-17938DiVA, id: diva2:589151
Conference
Workshop on Biomedical Natural Language Processing (BioNLP 2012), Montreal, Canada, June 8, 2012
Available from: 2013-01-17 Created: 2013-01-17 Last updated: 2018-01-11Bibliographically approved
In thesis
1. PageRank in Evolving Networks and Applications of Graphs in Natural Language Processing and Biology
Open this publication in new window or tab >>PageRank in Evolving Networks and Applications of Graphs in Natural Language Processing and Biology
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is dedicated to the use of graph based methods applied to ranking problems on the Web-graph and applications in natural language processing and biology.

Chapter 2-4 of this thesis is about PageRank and its use in the ranking of home pages on the Internet for use in search engines. PageRank is based on the assumption that a web page should be high ranked if it is linked to by many other pages and/or by other important pages. This is modelled as the stationary distribution of a random walk on the Web-graph.

Due to the large size and quick growth of the Internet it is important to be able to calculate this ranking very efficiently. One of the main topics of this thesis is how this can be made more efficiently, mainly by considering specific types of subgraphs and how PageRank can be calculated or updated for those type of graph structures. In particular we will consider the graph partitioned into strongly connected components and how this partitioning can be utilized.

Chapter 5-7 is dedicated to graph based methods and their application to problems in Natural language processing. Specifically given a collection of texts (corpus) we will compare different clustering methods applied to Pharmacovigilance terms (5), graph based models for the identification of semantic relations between biomedical words (6) and modifications of CValue for the annotation of terms in a corpus.

In Chapter 8-9 we look at biological networks and the application of graph centrality measures for the identification of cancer genes. Specifically in (8) we give a review over different centrality measures and their application to finding cancer genes in biological networks and in (9) we look at how well the centrality of vertices in the true network is preserved in networks generated from experimental data.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2016
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 217
National Category
Mathematics
Research subject
Mathematics/Applied Mathematics
Identifiers
urn:nbn:se:mdh:diva-33459 (URN)978-91-7485-298-1 (ISBN)
Public defence
2016-12-08, Kappa, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Available from: 2016-10-24 Created: 2016-10-24 Last updated: 2016-11-23Bibliographically approved

Open Access in DiVA

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Other links

http://www.aclweb.org/anthology/W12-2413

Authority records BETA

Engström, ChristopherSilvestrov, Sergei

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