UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic (2014)
Islam Beltagy, Stephen Roller, Gemma Boleda, and Katrin Erk, and Raymond J. Mooney
We represent natural language semantics by combining logical and distributional information in probabilistic logic. We use Markov Logic Networks (MLN) for the RTE task, and Probabilistic Soft Logic (PSL) for the STS task. The system is evaluated on the SICK dataset. Our best system achieves 73% accuracy on the RTE task, and a Pearson's correlation of 0.71 on the STS task.
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In The 8th Workshop on Semantic Evaluation (SemEval-2014), pp. 796--801, Dublin, Ireland, August 2014.
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Islam Beltagy Ph.D. Student beltagy [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Stephen Roller Ph.D. Student roller [at] cs utexas edu