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UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic (2014)
I. 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.
View:
PDF
Citation:
In
The 8th Workshop on Semantic Evaluation (SemEval-2014)
, pp. 796--801, Dublin, Ireland, August 2014.
Bibtex:
@inproceedings{beltagy:semeval14, title={UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic}, author={I. Beltagy and Stephen Roller and Gemma Boleda and and Katrin Erk and Raymond J. Mooney}, booktitle={The 8th Workshop on Semantic Evaluation (SemEval-2014)}, month={August}, address={Dublin, Ireland}, pages={796--801}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127458", year={2014} }
People
I. Beltagy
Ph.D. Alumni
beltagy [at] cs utexas edu
Raymond J. Mooney
Faculty
mooney [at] cs utexas edu
Stephen Roller
Ph.D. Alumni
roller [at] cs utexas edu
Areas of Interest
Combining Logical and Distributional Semantics
Lexical Semantics
Natural Language Processing
Statistical Relational Learning
Labs
Machine Learning