Ander Soraluze

Detecting apposition for text simplification in basque

In this paper we have performed a study on Apposition in Basque and we have developed a tool to identify and to detect automatically these structures. In fact, it is necessary to detect and to code this structures for advanced NLP applications. In our case, we plan to use the Apposition Detector in our Automatic Text Simplification system. This Detector applies a grammar that has been created using the Constraint Grammar formalism. The grammar is based, among others, on morphological features and linguistic information obtained by a named entity recogniser. We present the evaluation of that grammar and moreover, based on a study on errors, we propose a method to improve the results. We also use a Mention Detection System and we combine our results with those obtained by the Mention Detector to improve the performance.

Enriching Basque Coreference Resolution System using Semantic Knowledge sources

In this paper we present a Basque coreference resolution system enriched with semantic knowledge. An error analysis carried out revealed the deficiencies that the system had in resolving coreference cases in which semantic or world knowledge is needed. We attempt to improve the deficiencies using two semantic knowledge sources, specifically Wikipedia and WordNet.

Coreference Resolution for the Basque Language with BART.

In this paper we present our work on Coreference Resolution in Basque, a unique language which poses interesting challenges for the problem of coreference. We explain how we extend the coreference resolution toolkit, BART, in order to enable it to process Basque. Then we run four different experiments showing both a significant improvement by extending a baseline feature set and the effect of calculating performance of hand-parsed mentions vs. automatically parsed mentions. Finally, we discuss some key characteristics of Basque which make it particularly challenging for coreference and draw a road map for future work.