Received: 22-07-2015
Accepted: 03-09-2015
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X.ent Package for Extraction of Entities, Relationships between Entities and Support Data Analysis in Epidemiological Journals in French Agriculture
Abstract
Entity extraction is a task of information extraction and element classification in text such as the names of persons, organizations, locations, times, etc. and to find relationship between entities such as the relationship between the names of persons with the organizations. The X.ent tool was built solve this task. It uses dictionaries matching and hand - crafted rules to extract. In extracting the relationship between the entities, we applied two methods: analysis of text structures and unsupervised learning approach called coo – ccurrence analysis. This tool is available on the site of R at the links: http: //cran.r - project.org/web/packages/x.ent/index.html.
References
Abacha A.B., Zweigenbaum P. etMax A. (2012). Extraction d’information automatique en domaine médical par projection inter - langue: vers unpassage à l’échelle (Automatic Information Extraction in the Medical Domain by Cross - Lingual Projection) [in French]. La conférence JEP - TALN - RECITAL 2012, volume 2: TALN, p. 15 - 28.
Carpenter B. (2007). LingPipe for 99.99% Recall of Gene Mentions. Proceedings of the 2nd BioCreative workshop, Valencia, Spain.
Constant M., Tellier I., Duchier D., Dupont Y., Sigogne A. et Billot S. (2011). Intégrer des connaissances linguistiques dans unCRF: application à l’apprentissage d’un segmenteur - étiqueteur du français. TALN. Montpellier, p. 1 - 12.
Faure C., Delprat S., Mille A. etBoulicaut J. - F. (2006). Utilisation des réseaux bayésiens dans le cadre de l’extraction de règles d’association. Actes 6ème Journées Francophones Extraction etGestion de Connaissances EGC’06, p. 569 - 580.
Finkel J.R., Grenager T. and Manning C. (2005). Incorporating Non - local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (Stroudsburg, PA, USA, 2005), p. 363 - 370.
http1Stackoverflow (2014). http: //stackoverflow.com.
http2Manuel d’Utilisateur « Writing R Extentions » (2014). http://cran.r-project.org/doc/manuals/R-exts.html.
http3O beautiful code, « How R Searches and Finds Stuff » (2014). http://obeautifulcode.com/R/How-R-Searches-And-Finds-Stuff/.
http4Précision et rappel (2007). http://benhur.teluq.ca/SPIP/inf6104/article.php3?id_article = 98&id_rubrique =10&sem = Semaine%208.
http5Wilkipedia (2014). http://fr.wikipedia.org.
http6Les Résaux Bayésiens (2014). http://w3.jouy.inra.fr/unites/miaj/public/matrisq/Contacts/abari.07_ 03_12. expo2.pdf
http7Traitement Automatique du Langage Naturel (2014). http://lipn.univ-paris13.fr/~audibert/pages/enseignement /TAL_ITCN.pdf.
http8Stanford Named Entity Recognizer (2014).http://nlp.stanford.edu/software/CRF-NER.shtml.
http9LingPipe (2014)http://alias-i.com/lingpipe/.
http10Information Extraction And Named Entity Recognition (2014). https://web.stanford.edu/class/cs124/lec/ Information_Extraction_and_Named_Entity_Recognition.pdf.
http11Les Réseaux Bayésienes. http://www.bayesia.com/fr/technologie/reseaux-bayesiens.php.
Lafferty J., McCallum A. etPereira F. C. N. (2001). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Dep. Pap. CIS.
Moncla L. (2013). Automatic Annotation of Motion Expressions and Place Named Entities. 2nd Unitex/GramLab.
Paumier S. etMartineau C. (2006). Manuel d’Utilisateur Unitex 3.1 Beta. Université Paris - EstMarne - la - Vallée. version1.2.
Sutton C. etMcCallum A. (2010). An Introduction to Conditional Random Fields for Relational Learning. 1011.4088 [stat], p. 5 - 32.
R Development Core Team, R (2015). A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, ISBN 3 - 900051 - 07 - 0 (2015). URL http: //www.R - project.org/
Tannier X. (2012). Traitement Automatique des Langue. Université Paris - Sud.
Turenne N. (2013). Knowledge Needs and Information Extraction. Wiley - ISTE.
Zettlemoyer L. (2012). Relation Extraction. University of Washington.