State-of-the-art methods in automatic summarization rely almost exclusively on extracting salient sentences from input texts. Such extractive methods succeed in producing summaries which capture salient information but fail to produce fluent and coherent summaries. Recent progress in robust semantic analysis makes the application of semantic techniques to summarization relevant. We review in th...

Latent Semantic Analysis has only recently been applied to textual entailment recognition. However, these efforts have suffered from inadequate bag of words vector representations. Our prototype implementation for the Third Recognising Textual Entailment Challenge (RTE-3) improves the approach by applying it to vector representations that contain semi-structured representations of words. It use...

Textual entailment is normally regarded as a deeper analysis issue among other NLP techniques. Most textual entailment approaches employ deeper syntactic and semantic analyses. In contrast to such approaches, we used a simple, but fundamentally important, keyword based technique. Our system architecture was built on our observation that many of textual entailment issues are knowledge search iss...

This paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-seven acoustic features are extracted from the speech input. Two different classifiers Support Vector Machines (SVMs) and BP neural network are adopted to classify the emotional states. In text analysis, we use the two-step classification method to recognize ...

This paper introduces the methods employed by University of Houston team participating in the CL-SciSumm 2017 Shared Task at BIRNDL 2017 to identify reference spans in a reference document given sentences from citing papers. The following approaches were investigated: structural correspondence learning, positional language models, and textual entailment. In addition, we refined our methods from...

We propose new parse-free event-based features to be used in conjunction with lexical, syntactic, and semantic features of texts and hypotheses for Machine Learning-based Recognizing Textual Entailment. Our new similarity features are extracted without using shallow semantic parsers, but still lexical and compositional semantics are not left out. Our experimental results demonstrate that these ...

This paper presents the first round of the task on Cross-lingual Textual Entailment for Content Synchronization, organized within SemEval-2012. The task was designed to promote research on semantic inference over texts written in different languages, targeting at the same time a real application scenario. Participants were presented with datasets for different language pairs, where multi-direct...

In this paper we report the results obtained in the Semantic Textual Similarity (STS) task, with a system primarily developed for textual entailment. Our results are quite promising, getting a run ranked 39 in the official results with overall Pearson, and ranking 29 with the Mean metric.

Discourse references, notably coreference and bridging, play an important role in many text understanding applications, but their impact on textual entailment is yet to be systematically understood. On the basis of an in-depth analysis of entailment instances, we argue that discourse references have the potential of substantially improving textual entailment recognition, and identify a number o...

In this paper we propose a general method for the combination of specialized textual entailment engines. Each engine is supposed to address a specific language phenomenon, which is considered relevant for drawing semantic inferences. The model is based on the idea that the distance between the Text and the Hypothesis can be conveniently decomposed into a combination of distances estimated by si...