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

被引：0

作者：

Lavalle, Jesus ^{[1
,2
]}

Montes, Manuel ^{[1
]}

Jimenez, Hector ^{[3
]}

Villasenor, Luis ^{[1
]}

Beltran, Beatriz ^{[2
]}

机构：

[1] Inst Nacl Astrofis Opt & Electr, Coordinac Ciencias Computac, Santa Maria Tonanzintla, Mexico

[2] Benemerita Univ Autonoma Puebla, Fac Ciencias Comp, Puebla, Mexico

[3] Univ Autonoma Metropolitana, Unidad Cuajimalpa, Div Ciencias Comunicac & Diseno, Dept Tecnol Informac, Ciudad De Mexico, Mexico

来源：

COMPUTACION Y SISTEMAS | 2018年 / 22卷 / 01期

关键词：

Textual entailment; automatic theorem proving; natural logic;

D O I：

10.13053/CyS-22-1-2778

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：119 / 135

页数：17

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