• Corpus ID: 858065

The Sixth PASCAL Recognizing Textual Entailment Challenge

@inproceedings{Bentivogli2009TheSP,
  title={The Sixth PASCAL Recognizing Textual Entailment Challenge},
  author={Luisa Bentivogli and Peter Clark and Ido Dagan and Danilo Giampiccolo},
  booktitle={TAC},
  year={2009}
}
This paper presents the Sixth Recognizing Textual Entailment (RTE-6) challenge. This year a major innovation was introduced, as the traditional Main Task was replaced by a new task, similar to the RTE-5 Search Pilot, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario. A subtask was also proposed, aimed at detecting novel information. To continue the effort of testing RTE in NLP applications, a KBP Validation Pilot Task was set up, in which RTE systems… 
The Seventh PASCAL Recognizing Textual Entailment Challenge
TLDR
This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge, which replicated the exercise proposed in RTE-6, consisting of a Main Task, a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task.
JU_CSE_TAC: Textual Entailment Recognition System at TAC RTE-6
TLDR
The note describes the Recognizing Textual Entailment (RTE) system developed at the Computer Science and Engineering Department, Jadavpur University, India, which is based on the Support Vector Machine that uses twenty five features for lexical similarity.
A Textual Entailment System using Anaphora Resolution
TLDR
The note describes the Recognizing Textual Entailment (RTE) system developed at the Computer Science and Engineering Department, Jadavpur University, India, which is based on pre-processing task which includes Anaphora Resolution using JavaRAP tool.
Chapter 1 Recognizing Textual Entailment
Since 2005, researchers have worked on a broad task called Recognizing Textual Entailment (RTE), which is designed to focus efforts on general textual inference capabilities, but without constraining
An Empirical Study of Recognizing Textual Entailment in Japanese Text
TLDR
Experimental results achieved on benchmark data sets show that the machine-learning-based RTE system outperforms the baseline method based on lexical matching and suggest that the Machine Translation component can be utilized to improve the performance of the Rte system.
Design and realization of a modular architecture for textual entailment
TLDR
The novel EXCITEMENT architecture, which was developed to enable and encourage the consolidation of methods and resources in the textual entailment area, decomposes RTE into components with strongly typed interfaces, and is available as open source software under the GNU General Public License.
Recognizing Textual Entailment
TLDR
A number of researchers appear to have converged on some defining characteristics of the problem, and on characteristics of practical approaches to solving it, which are an exciting time to be working in this area.
“Ask Not What Textual Entailment Can Do for You...”
TLDR
It is argued that the single global label with which RTE examples are annotated is insufficient to effectively evaluate RTE system performance; to promote research on smaller, related NLP tasks, it is believed more detailed annotation and evaluation are needed.
Reasoning-Driven Question-Answering for Natural Language Understanding
TLDR
This thesis proposes a formulation for abductive reasoning in natural language and shows its effectiveness, especially in domains with limited training data, and presents the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use.
Using Textual Entailment with Variables for KBP Slot Filling Task
This report presents our extension of the textual entailment paradigm, in which variables are incorporated in the hypothesis, and are lled with extracted information during the entailment recognition
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