Czech named entity corpus and SVM-based recognizer
Jana Kravalová, Zdeněk Žabokrtský · 2009
This paper deals with recognition of named entities in Czech texts. We present a recently released corpus of Czech sentences with manually annotated named entities, in which a rich two-level classification scheme was used. There are around 6000 sentences in the corpus with roughly 33000 marked named entity instances. We use the data for training and evaluating a named entity recognizer based on Support Vector Machine classification technique. The presented recognizer outperforms the results previously reported for NE recognition in Czech.