we present a rule-based answer validation (AV) system based on textual entailment (TE) recognition mechanism that uses semantic features expressed in the Universal Networking Language (UNL). We consider the question as the TE hypothesis (H) and the supporting text as TE text (T). Our proposed TE system compares the UNL relations in both T and H in order to identify the entailment relation as ei...

When producing textual descriptions, humans express propositions regarding an object; but what do they express when annotating a document with simple tags? To answer this question, we have studied what users of tagging systems would have said if they were to describe a resource with fully fledged text. In particular, our work attempts to answer the following questions: if users were to use full...

The connections among natural language processing and argumentation theory are becoming stronger in the latest years, with a growing amount of works going in this direction, in different scenarios and applying heterogeneous techniques. In this paper, we present two datasets we built to cope with the combination of the Textual Entailment framework and bipolar abstract argumentation. In our appro...

We address a core aspect of the multilingual content synchronization task: the identification of novel, more informative or semantically equivalent pieces of information in two documents about the same topic. This can be seen as an application-oriented variant of textual entailment recognition where: i) T and H are in different languages, and ii) entailment relations between T and H have to be ...

We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks. Our models perform Knowledge Base Completion by learning to represent and compose diverse feature types from partially aligned and noisy resources. We perform experiments on Freebase utilizing additional entity type information and syntactic textual relations. Our evaluation suggests that the proposed mode...

The specification of a policy is performed in a policy language, usually following a textual representation. However, humans process images faster than text and they are prepared to process information presented in two or more dimensions: sometimes it is easier to explain things using figures and their graphical relations than writing textual representations. This paper describes a visual langu...

In this thesis, we present a novel approach for modeling textual entailment using lexicalsemantic information on the level of predicate-argument structure. To this end, we adopt information provided by the Berkeley FrameNet repository and embed it into an implemented end-to-end system. The two main goals of this thesis are the following: (i) to provide an analysis of the potential contribution ...

The aim of the current study is to propose a system, which can automatically deduce entailment relations of textual pairs. The system mainly uses seven features and a decision tree is utilized as a prediction model of the system and seven features of textual pairs are employed to be input of the prediction model. The experimental results for dataset Formal-run based on our proposed method are e...

In this study, the researcher chose three different vocabulary techniques (Visual Representation, Textual Enhancement, and Glossing) and compared them with traditional method of teaching vocabulary. 80 advanced EFL Learners were assigned as four intact groups (three experimental and one control group) through using a proficiency test and a vocabulary test as a pre-test. In the visual group, stu...

Biomedical relations are often expressed between entities occurring within the same sentence through syntactic means. However, a significant portion of such relations (in particular, causal relations) are expressed implicitly across sentence boundaries. Inferring these discourse-level relations can be challenging in the absence of syntactic clues. In this paper, we present a study of textual ch...