Metadiscourse is an important concept in the field of discourse analysis, but related studies have been confined to various genres written discourse. There little research on persuasive function metadiscourse speech. This paper adopts a data analysis method collect keynote speeches by Chinese President Xi Jinping at opening ceremonies 1th 4th China International Import Expo and Jinping’s New Ye...

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...

This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year’s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output...

This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2).1 We describe the RTE2 dataset and overview the submissions for the challenge. One of the main goals for this year’s dataset was to provide more “realistic” text-hypothesis examples, based mostly on outputs of actual systems. The 23 submissions for the challenge present diverse approaches and research direct...

We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and...