Learning Rules for Textual Entailment

The design of models that learn textual entailment recognizers from annotated examples
is not simple as it requires the modeling of semantics involved in the interaction of pairs
of text fragments. In this talk, we firstly introduce the class of pair feature spaces which
allow supervised machine learning algorithms to derive first-order rewrite rules from annotated
examples. In particular, we propose the syntactic and the shallow semantic pair
feature spaces.

Speaker Details

Fabio Massimo Zanzotto is an associate professor at the University of Rome “Tor Vergata”. He has been working in building models for robust syntactic parsing and for knowledge acquisition from corpora. In the last three years, he worked on the definition of textual entailment recognition models. He mainly explored the application of supervised machine learning models to learn inference rules from annotated examples. He participated in the first and the second Pascal RTE challenges with two different systems that explore different models for the use of supervised learning algorithms to the Textual Entailment recognition problem.

Fabio Massimo Zanzotto
University of Rome "Tor Vergata"