Edit-based text simplification systems have attained much attention in recent years due to their ability to produce simplification solutions that are interpretable, as well as requiring less training examples compared to traditional seq2seq systems.
When teaching language for specific purposes (LSP) linguistic resources are needed to help students understand and write specialised texts.
Natural Language Processing tools and resources have been so far mainly created and trained for standard varieties of language.
In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training.
In this paper, we present the MultiAzterTest tool: (i) an open source NLP tool which analyzes texts on over 125 measures of cohesion, language, and readability for English, Spanish and Basque, but whose architecture is designed to easily adapt other languages; (ii) readability assessment classifiers that improve the performance of Coh-Metrix in English, Coh-Metrix-Esp in Spanish and ErreXail in Basque; iii) a web tool.
Language resources are necessary for language processing, but building them is costly, involves many researches from different areas and needs constant updating.
The ability to read and understand written texts plays an important role in education, above all in the last years of primary education.
In this context, we propose a new semi-automatic approach to model the knowledge about properties and attributes in SUMO by exploiting the information encoded in WordNet adjectives and its mapping to SUMO.
Our final objective is the extraction of some guidelines towards a better exploitation of this commonsense knowledge framework by the improvement of the included resources.
In this paper, we investigate the application of the Closed World Assumption (CWA) to enable a better exploitation of FOL ontologies by using state-of-the-art automated theorem provers.
This paper presents a Basque corpus where Verbal Multiword Expressions (VMWEs) were annotated following universal guidelines.
In this paper, we present a comparative analysis of statistically predictive syntactic features of complexity and the treatment of these features by humans when simplifying texts.