This paper describes the Cambridge submission to the SemEval-2010 Parser Evaluation using Textual Entailment (PETE) task. We used a simple definition of entailment, parsing both T and H with the C&C parser and checking whether the core grammatical relations (subject and object) produced for H were a subset of those for T. This simple system achieved the top score for the task out of those syste...

This paper explores how a battery of unsupervised techniques can be used in order to create large, high-quality corpora for textual inference applications, such as systems for recognizing textual entailment (TE) and textual contradiction (TC). We show that it is possible to automatically generate sets of positive and negative instances of textual entailment and contradiction from textual corpor...

Component 2: Supporting Historical Evidence 1 Point: There are no specific historical textual evidence provided and no evidence of having done any reading and assimilating of secondary or primary sources, depending upon the nature of the written assignment. 2 Points: There are a few historical textual examples given, but more specific evidence and citation is needed to develop paper fully. 3 Po...

1. introduction khaghani shirvani is a well-known poet and one of the leading poets in the history of persian literature. all through his poetry books, there are numerous references to various scientific and technological terms that were popular at the time. in addition, the lyrics of khaghani abound with historical and religious allusions. his allusions to islamic and quranic narratives indica...

Deep models are the defacto standard in visual decision problems due to their impressive performance on a wide array of visual tasks. On the other hand, their opaqueness has led to a surge of interest in explainable systems. In this work, we emphasize the importance of model explanation in various forms such as visual pointing and textual justification. The lack of data with justification annot...

In this work we investigate methods to enable the detection of a specific type of textual entailment (strict entailment), starting from the preliminary assumption that these relations are often clearly expressed in texts. Our method is a statistical approach based on what we call textual entailment patterns, prototypical sentences hiding entailment relations among two activities. We experimente...