A string proximity-based approach to result ranking for terminological search
Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki, Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, Tome 156 (2014) no. 1, pp. 12-21 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice du chapitre de livre

In this article, a modification of the full-text approach to information retrieval is carried out by using semantic information about technical terms in text. The question of choosing a ranking metric for this type of search is raised. Several existing ranking metrics are discussed, and a new metric is proposed, which uses the features of terminological information retrieval. The proposed ranking metrics are experimentally compared, and the efficiency of the search system itself is estimated.
Keywords: information retrieval, ranking, technical terms.
@article{UZKU_2014_156_1_a1,
     author = {D. A. Zaikin},
     title = {A string proximity-based approach to result ranking for terminological search},
     journal = {U\v{c}\"enye zapiski Kazanskogo universiteta. Seri\^a Fiziko-matemati\v{c}eskie nauki},
     pages = {12--21},
     year = {2014},
     volume = {156},
     number = {1},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/UZKU_2014_156_1_a1/}
}
TY  - JOUR
AU  - D. A. Zaikin
TI  - A string proximity-based approach to result ranking for terminological search
JO  - Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki
PY  - 2014
SP  - 12
EP  - 21
VL  - 156
IS  - 1
UR  - http://geodesic.mathdoc.fr/item/UZKU_2014_156_1_a1/
LA  - ru
ID  - UZKU_2014_156_1_a1
ER  - 
%0 Journal Article
%A D. A. Zaikin
%T A string proximity-based approach to result ranking for terminological search
%J Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki
%D 2014
%P 12-21
%V 156
%N 1
%U http://geodesic.mathdoc.fr/item/UZKU_2014_156_1_a1/
%G ru
%F UZKU_2014_156_1_a1
D. A. Zaikin. A string proximity-based approach to result ranking for terminological search. Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki, Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, Tome 156 (2014) no. 1, pp. 12-21. http://geodesic.mathdoc.fr/item/UZKU_2014_156_1_a1/

[1] Roberts L. G., “Beyond Moore's Law: Internet Growth Trends”, Computer, 33:1 (2000), 117–119 | DOI

[2] Ding C. H., Buyya R., “Guided Google: A Meta Search Engine and its Implementation Using the Google Distributed Web Services”, Int. J. Comput. Appl., 26:3 (2004), 181–187

[3] Koster C. H. A., Seibert O., Seutter M., “The PHASAR search engine”, Proc. 11th Int. Conf. on Applications of Natural Language to Information Systems, Springer-Verlag, Berlin, 2006, 141–152

[4] Egozi O., Markovitch S., Gabrilovich E., “Concept-Based Information Retrieval Using Explicit Semantic Analysis”, ACM Trans. Inf. Syst., 29:2 (2011), Article No 8, 34 pp. | DOI

[5] Johannsson D. V., Biomedical information retrieval based on document-level term boosting, Ph. D. Thesis, Norwegian University of Science and Technology, Trondheim, Norway, 2009, 69 pp.

[6] Ramampiaro H., “Retrieving BioMedical Information with BioTracer: Challenges and Possibilities”, Proc. Norsk Informatikk Konferanse (NIK 2009), Tapir, Trondheim, Norway, 2009, 49–60

[7] Ramampiaro H., Li C., “Supporting BioMedical Information Retrieval: The BioTracer Approach”, Transactions on Large-Scale Data- and Knowledge-Centered Systems IV, Lecture Notes in Computer Science, 6990, 2011, 73–94 | DOI

[8] Manning C. D., Raghavan P., Schutze H., Introduction to Information Retrieval, Cambridge Univ. Press, Cambridge, 2008, 482 pp. | Zbl

[9] Smiley D., Pugh D. E., Apache Solr 3 Enterprise Search Server. From technologies to solutions, Packt Publishing, 2011, 418 pp.

[10] DB-Engines Ranking of Search Engines, URL: http://db-engines.com/en/ranking/search+engine

[11] Page L., Brin S., Motwani R., Winograd T., The PageRank citation ranking: Bringing order to the web, Stanford InfoLab, Stanford, 1998, 17 pp.

[12] Kondrak G., “$N$-gram similarity and distance”, String Processing and Information Retrieval, Lecture Notes in Computer Science, 3772, Springer, Berlin, 2005, 115–126 | DOI | MR

[13] Mikhalevich V. S., Slovar po kibernetike, Gl. red. Ukr. sov. entsikl. im. M. P. Bazhana, Kiev, 1989, 751 pp.

[14] Tang J., Arni T., Sanderson M., Clough P., “Building a diversity featured search system by fusing existing tools”, Proc. 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access, Springer-Verlag, Berlin–Heidelberg, 2008, 560–567

[15] Apache Solr 4.2.1, URL: http://lucene.apache.org/solr/4_2_1/

[16] Goldberg J. H., Stimson M. J., Lewenstein M., Scott N., Wichansky A. M., “Eye tracking in web search tasks: design implications”, Proc. 2002 symposium on Eye tracking research applications, ACM, N.Y., 2002, 51–58

[17] Anick P., “Using terminological feedback for web search refinement: a log-based study”, Proc. 26th annual int. ACM SIGIR conference on Research and development in informaion retrieval, ACM, N.Y., 2003, 88–95 | DOI

[18] Aizawa A., “An information-theoretic perspective of tf-idf measures”, Information Processing and Management, 39:1 (2003), 45–65 | DOI | MR | Zbl