COGEX at RTE 3

Applying COGEX to Recognize Textual Entailment

The PASCAL RTE challenge has helped LCC to explore the applicability of enhancements that have been made to our logic form representation and WordNet lexical chains generator. Our system transforms each T-H pair into logic form representation with semantic relations. The system automatically generates NLP axioms serving as linguistic rewriting rules and lexical chain axioms that connect concept...

Automatic Answer Validation using COGEX

A Statistics-Based Semantic Textual Entailment System

We present a Textual Entailment (TE) recognition system that uses semantic features based on the Universal Networking Language (UNL). The proposed TE system compares the UNL relations in both the text and the hypothesis to arrive at the two-way entailment decision. The system has been separately trained on each development corpus released as part of the Recognizing Textual Entailment (RTE) comp...

Machine Learning Based Semantic Inference: Experiments and Observations at RTE-3

Textual Entailment Recognition is a semantic inference task that is required in many natural language processing (NLP) applications. In this paper, we present our system for the third PASCAL recognizing textual entailment (RTE-3) challenge. The system is built on a machine learning framework with the following features derived by state-of-the-art NLP techniques: lexical semantic similarity (LSS...

A Textual Entailment System using Anaphora Resolution

The note describes the Recognizing Textual Entailment (RTE) system developed at the Computer Science and Engineering Department, Jadavpur University, India. In this competition, we have participated and submitted the results in the RTE-7 Main Task (3 runs), Novelty Task (3 runs) and RTE-7 KBP Validation task (2 unique runs for generic task and 2 unique runs for tailored task). For the RTE7 Main...