In this paper we argue that researchers in Intelligent Data Analysis (IDA) in Medicine and Pharmacology should consider textual databases as an additional source of knowledge. We describe three areas where medical knowledge extraction from textual databases can be fruitful: finding new applications for existing drugs, evolution of medical knowledge in time, and drug risk assessment. We evaluate...

This paper describes the Bar-Ilan system participating in the Recognising Textual Entailment Challenge. The paper proposes first a general probabilistic setting that formalizes the notion of textual entailment. We then describe a concrete alignment-based model for lexical entailment, which utilizes web co-occurrence statistics in a bag of words representation. Finally, we report the results of ...

The primary focuses of this entry this year was firstly, to develop a framework to allow multiple researchers from our group to easily contribute metrics measuring textual entailment, and secondly, to provide a baseline which we could use in our tools to evaluate and compare new metrics. A development environment tool was created to quickly allow for testing of various metrics and to easily ran...

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 ...