this research is intended to present an elucidation of interaction between textual context and circumstantial context while comprehending the text. this interaction is maintained in a specific hermeneutic circle of comprehending the text. the qur’ān is divine word and has characteristics that demands hermeneutic circle proportionate to it. therefore, in this research the characteristics influen...

In this dissertation, I tackle the challenge of information retrieval for multivariate research data by providing novel means of content-based access. Large amounts of multivariate data are produced and collected in different areas of scientific research and industrial applications, including the human or natural sciences, the social or economical sciences and applications like quality control,...

In this paper, we present our system description in task of Cross-lingual Textual Entailment. The goal of this task is to detect entailment relations between two sentences written in different languages. To accomplish this goal, we first translate sentences written in foreign languages into English. Then, we use EDITS, an open source package, to recognize entailment relations. Since EDITS only ...

For textual entailment recognition systems, it is often important to correctly handle Generalized quantifiers (GQ). In this paper, we explore ways of encoding GQs in a recent framework of Dependency-based Compositional Semantics, especially aiming to correctly handle linguistic knowledge like hyponymy when GQs are involved. We use both the selection operator mechanism and a new relation extensi...

We introduce a novel type of graphs for NLP – Textual Entailment Graphs (TEG). We present the first gold-standard dataset of TEGs, describe a methodology we developed for their construction and evaluate state-of-the-art RTE engines for this task. We show the utility of TEGs for text exploration and suggest that TEGs might have different usages and that their automatic creation is an interesting...

This paper describes the CLEAR team’s submission to the 2008 Text Analysis Conference under the Recognizing Textual Entailment track. The system breaks text fragments down into fine-grained semantic facets and performs entailment recognition on these. We show that, in the relevant subset of the data, we can achieve 90% accuracy in pinpointing the specific facet of a hypothesis that is not entai...

The Text Analysis Conference (TAC) 2009 competition featured a new textual entailment search task, which extends the 2008 textual entailment task. The goal is to find information in a set of documents that are entailed from a given statement. Rather than designing a system specifically for this task, we investigated the adaptation of an existing artificial believer system to solve this task. Th...

Blogs and forums are widely adopted by online communities to debate about various issues. However, a user that wants to cut in on a debate may experience some difficulties in extracting the current accepted positions, and can be discouraged from interacting through these applications. In our paper, we combine textual entailment with argumentation theory to automatically extract the arguments fr...

The paper presents a meta-learning approach for textual document classification task and an automatic selection of the best available algorithm for creation of classifiers. After brief introductory description of principles of document preprocessing, creation and evaluation of the classifiers, the metalearning approach is presented as a method for automatic selection of the most appropriate cla...