This paper describes our system of recognizing textual entailment for RITE Traditional and Simplified Chinese subtasks at NTCIR10. We build a textual entailment recognition framework and implement a system that employs features of three categories, including string, structure and linguistic features, for the recognition. In addition, an entailment transformation approach is leveraged to align t...

This paper presents the modelling approaches performed by the FHDO Biomedical Computer Science Group for the compound figure detection and subfigure classification tasks at ImageCLEF 2015 medical classification. This is the first participation of the group at an accepted lab of the Cross Language Evaluation Forum. For image visual representation, various state-of-the-art visual features such as...

Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as “external” to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language st...

Question Answering (QA) is a task that deserves more collaboration between Natural Language Processing (NLP) and Knowledge Representation (KR) communities, not only to introduce reasoning when looking for answers or making use of answer type taxonomies and encyclopedic knowledge, but also, as discussed here, for Answer Validation (AV), that is to say, to decide whether the responses of a QA sys...

In this paper, we provide a statistical machine learning representation of textual entailment via syntactic graphs constituted by tree pairs. We show that the natural way of representing the syntactic relations between text and hypothesis consists in the huge feature space of all possible syntactic tree fragment pairs, which can only be managed using kernel methods. Experiments with Support Vec...

The name txtUML stands for textual, executable, translatable UML. It is an Eclipse-based tool built on top of JDT, Xtext/Xbase and Papyrus UML. The tool is designed for textual model editing. This makes storage, version control, compare and merge processes, editing and searching easier and more efficient. The tool supports two textual syntaxes for modeling: the standalone syntax, which is desig...

Topic models have been shown to be a useful way of representing the content of large document collections, for example via visualisation interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a set of keywords, i.e. the top-n words with highest marginal probability within the topic. However, alternativ...

In 2008 the Recognizing Textual Entailment Challenge (RTE-4) was proposed for the first time as a track at the Text Analysis Conference (TAC). Another important innovation introduced in this campaign was a three-judgment task, which required the systems to make a further distinction between pairs where the entailment does not hold because the content of H is contradicted by the content of T, an...

After defining what is understood by textual entailment and semantic equivalence, the present state and the desirable future of the systems aimed at recognizing them is shown. A compilation of the currently implemented techniques in the main Recognizing Textual Entailment and Semantic Equivalence systems is given.