Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model
Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of int...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/13/3/76 |
| _version_ | 1797241592901271552 |
|---|---|
| author | Eduard Puerto Jose Aguilar Angel Pinto |
| author_facet | Eduard Puerto Jose Aguilar Angel Pinto |
| author_sort | Eduard Puerto |
| collection | DOAJ |
| description | Currently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of interest. In particular, an interesting approach is to computationally simulate the brain process for recognizing misspelled words and their automatic correction. Thus, this article presents an automatic recognition and correction system of misspelled words in Spanish texts, for the detection of misspelled words, and their automatic amendments, based on the systematic theory of pattern recognition of the mind (PRTM). The main innovation of the research is the use of the PRTM theory in this context. Particularly, a corrective system of misspelled words in Spanish based on this theory, called Ar2p-Text, was designed and built. Ar2p-Text carries out a recursive process of analysis of words by a disaggregation/integration mechanism, using specialized hierarchical recognition modules that define formal strategies to determine if a word is well or poorly written. A comparative evaluation shows that the precision and coverage of our Ar2p-Text model are competitive with other spell-checkers. In the experiments, the system achieves better performance than the three other systems. In general, Ar2p-Text obtains an F-measure of 83%, above the 73% achieved by the other spell-checkers. Our hierarchical approach reuses a lot of information, allowing for the improvement of the text analysis processes in both quality and efficiency. Preliminary results show that the above will allow for future developments of technologies for the correction of words inspired by this hierarchical approach. |
| first_indexed | 2024-04-24T18:25:47Z |
| format | Article |
| id | doaj.art-858457f962ac4ca3ba668a5a2eef7b91 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-24T18:25:47Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-858457f962ac4ca3ba668a5a2eef7b912024-03-27T13:32:02ZengMDPI AGComputers2073-431X2024-03-011337610.3390/computers13030076Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network ModelEduard Puerto0Jose Aguilar1Angel Pinto2Grupo de Investigación en Inteligencia Artificial (GIA), Facultad de Ingeniería, Universidad Francisco de Paula Santander, Cúcuta 540001, ColombiaCentro de Estudio en Microcomputación y Sistemas Distribuidos (CEMISID), Facultad de Ingeniería, Universidad de Los Andes, Mérida 5101, VenezuelaGrupo de Investigación TESEEO, Universidad del Sinú, Montería 230001, ColombiaCurrently, approaches to correcting misspelled words have problems when the words are complex or massive. This is even more serious in the case of Spanish, where there are very few studies in this regard. So, proposing new approaches to word recognition and correction remains a research topic of interest. In particular, an interesting approach is to computationally simulate the brain process for recognizing misspelled words and their automatic correction. Thus, this article presents an automatic recognition and correction system of misspelled words in Spanish texts, for the detection of misspelled words, and their automatic amendments, based on the systematic theory of pattern recognition of the mind (PRTM). The main innovation of the research is the use of the PRTM theory in this context. Particularly, a corrective system of misspelled words in Spanish based on this theory, called Ar2p-Text, was designed and built. Ar2p-Text carries out a recursive process of analysis of words by a disaggregation/integration mechanism, using specialized hierarchical recognition modules that define formal strategies to determine if a word is well or poorly written. A comparative evaluation shows that the precision and coverage of our Ar2p-Text model are competitive with other spell-checkers. In the experiments, the system achieves better performance than the three other systems. In general, Ar2p-Text obtains an F-measure of 83%, above the 73% achieved by the other spell-checkers. Our hierarchical approach reuses a lot of information, allowing for the improvement of the text analysis processes in both quality and efficiency. Preliminary results show that the above will allow for future developments of technologies for the correction of words inspired by this hierarchical approach.https://www.mdpi.com/2073-431X/13/3/76spell-checkertext recognitionAr2p-Text |
| spellingShingle | Eduard Puerto Jose Aguilar Angel Pinto Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model Computers spell-checker text recognition Ar2p-Text |
| title | Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model |
| title_full | Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model |
| title_fullStr | Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model |
| title_full_unstemmed | Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model |
| title_short | Automatic Spell-Checking System for Spanish Based on the Ar2p Neural Network Model |
| title_sort | automatic spell checking system for spanish based on the ar2p neural network model |
| topic | spell-checker text recognition Ar2p-Text |
| url | https://www.mdpi.com/2073-431X/13/3/76 |
| work_keys_str_mv | AT eduardpuerto automaticspellcheckingsystemforspanishbasedonthear2pneuralnetworkmodel AT joseaguilar automaticspellcheckingsystemforspanishbasedonthear2pneuralnetworkmodel AT angelpinto automaticspellcheckingsystemforspanishbasedonthear2pneuralnetworkmodel |