Geodesic
A software system for automatic construction of a semantic word network
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 6 (2017) no. 2, pp. 69-83 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

A semantic word network is a network that represents the semantic relations between individual words or their lexical senses. In this paper, we present a software system for automatic construction of a semantic wordnetwork. The system, called SWN, is designed for the construction for such a semantic word network and includesthe implementation of unsupervised concept discovery and semantic relation establishing methods as well as theimplementation of a supervised relation expansion method. The methods use widely available language resources,such as semantic relation dictionaries and background text corpora. The domain model has been presented usingthe VOWL notation. The system architecture is represented using the UML notation and is composed of theconcept discovery module, semantic relation construction module, the Semantic Web export module, and theevaluation dataset construction module based on microtask-based crowdsourcing. The present software system isopen source and is available for integration into third-party data mining systems.
Keywords: semantic network, lexical semantics, software engineering, free software, Semantic Web, UML.
Mots-clés : VOWL 
@article{VYURV_2017_6_2_a4,
     author = {D. A. Ustalov and A. V. Sozykin},
     title = {A software system for automatic construction of a semantic word network},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
     pages = {69--83},
     year = {2017},
     volume = {6},
     number = {2},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VYURV_2017_6_2_a4/}
}
TY  - JOUR
AU  - D. A. Ustalov
AU  - A. V. Sozykin
TI  - A software system for automatic construction of a semantic word network
JO  - Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
PY  - 2017
SP  - 69
EP  - 83
VL  - 6
IS  - 2
UR  - http://geodesic.mathdoc.fr/item/VYURV_2017_6_2_a4/
LA  - ru
ID  - VYURV_2017_6_2_a4
ER  - 
%0 Journal Article
%A D. A. Ustalov
%A A. V. Sozykin
%T A software system for automatic construction of a semantic word network
%J Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika
%D 2017
%P 69-83
%V 6
%N 2
%U http://geodesic.mathdoc.fr/item/VYURV_2017_6_2_a4/
%G ru
%F VYURV_2017_6_2_a4
D. A. Ustalov; A. V. Sozykin. A software system for automatic construction of a semantic word network. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 6 (2017) no. 2, pp. 69-83. http://geodesic.mathdoc.fr/item/VYURV_2017_6_2_a4/

[1] O. H. Goncalo, P. E. Gomes, P. T. Onto, “A Flexible Approach for Creating a Portuguese Wordnet Automatically”, Language Resources and Evaluation, 48:2 (2014), 373–393 | DOI

[2] N. V. Loukachevitch, Thesauri in Information Retrieval Tasks, MSU Publishing, Moscow, 2011, 512 pp.

[3] W. Wong, “Ontology Learning from Text: A Look Back and into the Future”, ACM Computing Surveys, 44:4 (2012), 1–36 | DOI

[4] R. Navigli, S. P. Ponzetto, “BabelNet: The Automatic Construction, Evaluation and Application of a Wide-Coverage Multilingual Semantic Network”, Artificial Intelligence, 193 (2012), 217–250 | DOI

[5] C. J. Camancho, M. T. Pilehvar, R. Navigli, “Nasari: Integrating Explicit Knowledge and Corpus Statistics for a Multilingual Representation of Concepts and Entities”, Artificial Intelligence, 240 (2016), 36–64 | DOI

[6] D. A. Ustalov, Concept Discovery from Synonymy Graphs. Computational Technologies. 2017. vol. 22, Special Issue 1. pp. 99–112. } {\tt http://depot.nlpub.ru/

[7] D. A. Ustalov, Contexts for Constructing a Semantic Word Network. Computational Linguistics and Intellectual Technologies: papers from the Annual conference “Dialogue” (Moscow, May 31–June 3, 2017). Moscow, RSUH, 2017. In press. } {\tt http://depot.nlpub.ru/

[8] D. A. Ustalov, N. V. Arefyev, C. Biemann, A. I. Panchenko, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Association for Computational Linguistics, 2017,P. 543–550 } {\tt https://aclweb.org/

[9] T. Berners-Lee, J. Hendler, O. Lassila, The Semantic Web. Scientific American. Vol. 284, No. 5. P. 28–37., 2001 } {\tt https://www.scientificamerican.com/

[10] S. Lohmann, et al., “et al. Visualizing Ontologies with VOWL”, Semantic Web, 2016, 399–419 | DOI

[11] M. van Assem et al., “A Method to Convert Thesauri to SKOS”, Proceedings. Springer Berlin Heidelberg, 3rd European Semantic Web Conference (ESWC 2006 Budva, Montenegro, June 11–14, 2006), 2006, 95–109 | DOI

[12] J. McCrae, D. Spohr, P. Cimiano, “Linking Lexical Resources and Ontologies on the Semantic Web with Lemon”, Proceedings, Part I. Springer Berlin Heidelberg, The Semantic Web: Research and Applications: 8th Extended Semantic Web Conference, ESWC 2011 (Heraklion, Crete, Greece, May 29–June 2, 2011), 2011, 245–259 | DOI

[13] D. A. Ustalov, Russian Thesauri as Linked Open Data. Computational Linguistics and Intellectual Technologies: papers from the Annual conference “Dialogue” (Moscow, May 27–30, 2015) } {\tt http://www.dialog-21.ru/

[14] F. Pedregosa, et al., Scikit-Learn: Machine Learning in Python // Journal of Machine Learning Research. 2011. Vol. 12. P. 2825–2830 } {\tt http://www.jmlr.org/papers/v12

[15] C. Biemann, Chinese Whispers: An Efficient Graph Clustering Algorithm and Its Application to Natural Language Processing Problems // Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing. Association for Computational Linguistics, 2006 } {\tt http://dl.acm.org/

[16] S. van Dongen, Graph Clustering by Flow Simulation. Ph.D. Thesis. University of Utrecht, 2000 } {\tt https://dspace.library.uu.nl/

[17] R. Rehurek, P. Sojka, Software Framework for Topic Modelling with Large Corpora // New Challenges for NLP Frameworks Programme: A workshop at LREC 2010. European Language Resources Association, 2010. P. 51–55 } {\tt https://radimrehurek.com/

[18] M. Abadi, et al., et al. TensorFlow: A System for Large-Scale Machine Learning // 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, 2016 } {\tt https://www.usenix.org

[19] A. A. Hagberg, D. A. Schult, P. J. Swart, Exploring Network Structure, Dynamics, and Function using NetworkX // Proceedings of the 7th Python in Science Conference. 2008 } {\tt http://conference.scipy.org/proceedings/scipy2008

[20] D. Beckett, “The Design and Implementation of the Redland RDF Application Framework”, Computer Networks, 2002, 577–588 | DOI

[21] M. Korobov, “Morphological Analyzer and Generator for Russian and Ukrainian Languages”, Analysis of Images, Social Networks and Texts, 4th International Conference, AIST 2015 (Yekaterinburg, Russia, April 9–11, 2015, Revised Selected Papers), Springer International Publishing, 2015, 320–332 | DOI

[22] C. D. Manning, P. Raghavan, H. Schutze, Introduction to Information Retrieval, Cambridge University Press, 2008, 506 pp.

[23] M. Riedl, C. Biemann, Unsupervised Compound Splitting With Distributional Semantics Rivals Supervised Methods // Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2016 } {\tt https://aclweb.org/

[24] R. Fu, et al., Learning Semantic Hierarchies via Word Embeddings. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2014 } {\tt https://aclweb.org/

[25] dustalov/watset: Concept Discovery from Synonymy Graphs } {\tt https://github.com/dustalov/watset

[26] dustalov/watlink: Concept Linking } {\tt https://github.com/dustalov/watlink

[27] dustalov/projlearn: Learning Word Subsumption Projections } {\tt https://github.com/dustalov/projlearn