Luis Villaseñor Pineda

Automatic Theorem Proving for Natural Logic: a Case Study on Textual Entailment

Recognizing Textual Entailment (RTE) is a Natural Language Processing task. It is very important in tasks as Semantic Search and Text Summarization. There are many approaches to RTE, for example, methods based on machine learning, linear programming, probabilistic calculus, optimization, and logic. Unfortunately, no one of them can explain why the entailment is carried on. We can make reasonings, with Natural Logic, from the syntactic part of a natural language expression, and very little semantic information. This paper presents an Automatic Theorem Prover for Natural Logic that allows to know precisely the relationships needed in order to reach the entailment in a class of natural language expressions.

Class-specific feature generation for 1NN through genetic programming

This paper introduces a genetic program for class-specific feature extraction for 1NN. Under the proposed method a new feature space is generated for each class in the problem under analysis. Where feature spaces are build by merging the initial features with a genetic program that aims at maximizing classification accuracy of a 1NN classifier. We compare the performance of our method to both, classical-standard techniques (e.g., PCA, LDA) and to solutions based on evolutionary algorithms. Experimental results reveal our method outperforms alternative solutions in a wide variety of data sets.