Richárd Farkas

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...

A manually annotated HTML corpus for a novel scientific trend analysis (2009)

Richárd Farkas, Róbert Ormándi, Márk Jelasity, János Csirik

Here we present a manually annotated corpus of web pages and annotation tool for Web Content Mining. The corpus is extensively annotated, has a hierarchical label structure and is freely available...

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...

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...

The strength of co-authorship in gene name disambiguation (2008)

Farkas, Richárd

Abstract Background A biomedical entity mention in articles and other free texts is often ambiguous. For example, 13% of the gene names (aliases) might refer to more than one gene. The task of Gene...

A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. Discovery Science 2006 (2006)

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...