Rule-Based Spanish Multiple Question Reformulation and their Classification using a Convolutional Neuronal Network
| Autor: | Alberto Iturbe Herrera, Dante Mujica Vargas, Noé Alejandro Castro Sánchez |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
business.industry Computer science Process (engineering) media_common.quotation_subject Rule-based system Part of speech computer.software_genre Convolutional neural network Order (exchange) Quality (business) Artificial intelligence business computer Word (computer architecture) Natural language processing Simple (philosophy) media_common |
| Zdroj: | Computación y Sistemas. 25 |
| ISSN: | 2007-9737 1405-5546 |
| DOI: | 10.13053/cys-25-1-3895 |
| Popis: | Question reformulation allows the creation of different forms of the same question in order to identify the best answer. However, when aspects suchas length and complexity increase, the reformulation process becomes more complicated, consequently also the recovery of the corresponding information. In this research, a method for the reformulation of multiple questions in Spanish is presented, as part of the pre-processing stage in a question-answer system. The lexical category of each word, Named Entities and Multi-Word Terms, were used to reformulate multiple questions into new individual questions, and then a Convolutional Neural Network was used to classify them, allowing to find or build adequate answers to improve the quality of the results, which is fundamental in QAsystems. A dataset with multiple questions was also created to evaluate our reformulation method, since it was not possible to find any. On the other hand, for the evaluation of the question classification model, we used the TREC, Simple Questions, Web Questions, Wiki Movies and Curated TREC datasets, translated into Spanish. Both tasks achieved promising results for further work. |
| Databáze: | OpenAIRE |
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