Kepa Bengoetxea

Application of feature propagation to dependency parsing

This paper presents a set of experiments performed on parsing the Basque Dependency Treebank. We have applied feature propagation to dependency parsing, experimenting the propagation of several morphosyntactic feature values. In the experiments we have used the output of a parser to enrich the input of a second parser. Both parsers have been generated by Maltparser, a freely data-driven dependency parser generator. The transformations, combined with the pseudoprojective graph transformation, obtain a LAS of 77.12% improving the best reported results for Basque.

On WordNet Semantic Classes and Dependency Parsing

This paper presents experiments with WordNet semantic classes to improve dependency parsing. We study the effect of semantic classes in three dependency parsers, using two types of constituencyto-dependency conversions of the English Penn Treebank. Overall, we can say that the improvements are small and not significant using automatic POS tags, contrary to previously published results using gold POS tags (Agirre et al., 2011). In addition, we explore parser combinations, showing that the semantically enhanced parsers yield a small significant gain only on the more semantically oriented LTH treebank conversion.

Detecting the Central Units of Brazilian Portuguese argumentative answer texts

Understanding or writing properly the main idea or the Central Unit (CU) of a text is a very important task in exams. So, detecting automatically the CU may be of interest in language evaluation tasks. This paper presents a CU detector based on machine learning techniques for argumentative answer texts in Brazilian Portuguese. Results show that the detection of CUs following machine learning techniques in argumentative answer texts is better that those using rules.

Un detector de la unidad central de un texto basado en técnicas de aprendizaje automático en textos científicos para el euskera

En este articulo presentamos el primer detector de la Unidad Central (UC) de resumenes cientificos en euskera basado en tecnicas de aprendizaje automatico. Despues de segmentar el texto en unidades de discurso elementales, la deteccion de la unidad central es crucial para anotar de forma mas fiable la estructura relacional de textos bajo la Teoria de la Estructura Retorica o Rhetorical Structure Theory (RST). Ademas, la unidad central puede ser explotada en diversas tareas como resumen automatico, tareas de pregunta y respuesta o analisis del sentimiento. Los resultados obtenidos demuestran que las tecnicas de aprendizaje automatico superan a las tecnicas basadas en reglas a pesar del pequeno tamano del corpus y de la heterogeneidad de los dominios que este muestra, dejando todavia lugar para mejoras y desarrollo.