GYDER: maxent metonymy resolution (2009)
Richárd Farkas, Eszter Simon, György Szarvas
Though the GYDER system has achieved the highest accuracy scores for the metonymy resolution shared task at SemEval-2007 in all six subtasks, we don’t consider the results (72.80 % accuracy for...
The BioScope corpus: biomedical texts annotated for uncertainty, (2009)
Bmc Bioinformatics, Veronika Vincze, György Szarvas, Richárd Farkas, György Móra, János Csirik
negation and their scopes
The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes (2008)
Vincze, Veronika, Szarvas, György, Farkas, Richárd, Móra, György, Csirik, János
Abstract Background Detecting uncertain and negative assertions is essential in most BioMedical Text Mining tasks where, in general, the aim is to derive factual knowledge from textual data. This...
Zoltán Alexin, János Csirik, György Szarvas, Márton Miháltz
inf.u-szeged.hu This report describes a recent Hungarian project begun in the spring of 2005. The goals of the project are to produce a Hungarian version of the EuroWordNet ontology database, to...
Automatic construction of rule-based ICD-9-CM coding systems (2008)
Farkas, Richárd, Szarvas, György
Abstract Background In this paper we focus on the problem of automatically constructing ICD-9-CM coding systems for radiology reports. ICD-9-CM codes are used for billing purposes by health...
Named Entity Recognition for Hungarian Using Various Machine Learning Algorithms (2008)
Richárd Farkas, György Szarvas, András Kocsor
In this paper we introduce a statistical Named Entity recognizer (NER) system for the Hungarian language. We examined three methods for identifying and disambiguating proper nouns (Artificial Neural...
György Szarvas, Richárd Farkas, András Kocsor
Abstract. In this paper we introduce a multilingual Named Entity Recognition (NER) system that uses statistical modeling techniques. The system identifies and classifies NEs in the Hungarian and...