Recognizing Textual Entailment using Description Logic and Semantic Relatedness Reda Siblini, Ph.D. Concordia University, 2014 Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true. Accurate approaches for textual entailment can be beneficial to various natural language processing (NLP) appl...

When two texts have an inclusion relation, the relationship between them is called entailment. The task of mechanically distinguishing such a relation is called recognising textual entailment (RTE), which is basically a kind of semantic analysis. A variety of methods have been proposed for RTE. However, when the previous methods were combined, the performances were not clear. So, we utilized ea...

The management of textual information is getting more and more attention within the case-based reasoning community. In this paper, we will address the question of how a case base can be obtained from a given textual description and how this representation scheme can be enriched by higher level concepts.

The management of textual information is getting more and more attention within the case-based reasoning community. In this paper, we will address the question of how a case base can be obtained from a given textual description and how this representation scheme can be enriched by higher level concepts.

Recognising Textual Entailment (RTE) has the objective of judging whether one piece of short text, normally a single sentence, follows from another piece of longer text. Such a task has been the focus of three consecutive challenges since 2005. Current approaches have explored the more sophisticated deep Natural Language Processing (NLP) techniques. However, the capability of shallow features a...

The main intrinsic evaluation for vector space representation has been focused on textual similarity, where the task is to predict how semantically similar two words or sentences are. We propose a novel framework, Story Cloze Evaluator, for evaluating vector representations which goes beyond textual similarity and captures the notion of predicting what should happen next given a context. This e...

We focus on the production of efficient descriptions of objects, actions and events. We define a type of efficiency, textual economy, that exploits the hearer's recognition of inferential links to material elsewhere within a sentence. Textual economy leads to efficient descriptions because the material that supports such inferences has been included to satisfy independent communicative goals, a...

Type-2 clones are duplicated passages of source code that have identical sequences of tokens (whitespace and comments are ignored) except for the textual representation of identifiers and literals—parameters. Commonly, type-2 clones are detected by simply abstracting from the parameters’ textual representation. This normalization, however, frequently results in similar but otherwise unrelated c...

This paper describes the visual representation of the Timing Definition Language (TDL), a high-level textual description language for timing aspects of embedded real-time systems. For this purpose we have designed and implemented the so-called TDL:VisualCreator tool. The paper presents the core concepts of TDL, which comprises now both the time-triggered and eventtriggered paradigm, and exempli...

The algorithmic generation of textual descriptions of real world image sequences requires conceptual knowledge. The algorithmic generation of synthetic image sequences from textual descriptions requires conceptual knowledge, too. An explicit representation formalism for behavioral knowledge based on formal logic is presented which can be utilized in both tasks – Understanding and Creation of vi...