Improving the Performance of a Named Entity Extractor by Applying a Stacking Scheme

Abstract. In this paper we investigate the way of improving the performance of a Named Entity Extraction (NEE) system by applying machine learning techniques and corpus transformation. The main resources used in our experiments are the publicly available tagger TnT and a corpus of Spanish texts in which named entities occurrences are tagged with BIO tags. We split the NEE task into two subtasks 1) Named Entity Recognition (NER) that involves the identification of the group of words that make up the name of an entity and 2) Named Entity Classification (NEC) that determines the category of a named entity. We have focused our work on the improvement of the NER task, generating four different taggers with the same training corpus and combining them using a stacking scheme. We improve the baseline of the NER task (Fβ=1 value

• Type: Conference
• Year: 2004
• Where: IBERAMIA
• Authors: José A. Troyano, Víctor J. Díaz, Fernando Enríquez, Luisa Romero
• Keywords: Artificial Intelligence, Entity Extraction, IBERAMIA 2004, Named Entity Recognition, NER Task