Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.13053/CyS-22-1-2778 | COMPUTACION Y SISTEMAS |
Keywords | Field | DocType |
Textual entailment, automatic theorem proving, natural logic | Automatic summarization,Logical consequence,Semantic search,Expression (mathematics),Textual entailment,Computer science,Automated theorem proving,Natural language,Artificial intelligence,Natural language processing,Probabilistic logic | Journal |
Volume | Issue | ISSN |
22 | 1 | 1405-5546 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José de Jesús Lavalle-Martínez | 1 | 0 | 2.70 |
| Manuel Montes-Y-Gómez | 2 | 638 | 83.97 |
| Héctor Jiménez-Salazar | 3 | 75 | 16.35 |
| Luis Villaseñor-Pineda | 4 | 403 | 53.74 |
| Beatríz Beltrán Martínez | 5 | 0 | 0.34 |