Unsupervised Word Sense Disambiguation Using Alpha-Beta Associative Memories
We present an alternative method to the use of overlap ping as a distance measure in simple Les k algorithm . This paper presents an algorithm that uses Alpha -Beta associative memory type Max and Min to measure a given ambiguous word’s meaning in relation to its context, assigning to the word the m...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | México |
| Institución: | Universidad de Guadalajara |
| Repositorio: | Redalyc-UDG |
| OAI Identifier: | oai:redalyc.org:402650526007 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=402650526007 |
| Access Level: | acceso abierto |
| Palabra clave: | Computación Alpha simple Lesk algorithm Word sense disambiguation Beta associative memories |
| Sumario: | We present an alternative method to the use of overlap ping as a distance measure in simple Les k algorithm . This paper presents an algorithm that uses Alpha -Beta associative memory type Max and Min to measure a given ambiguous word’s meaning in relation to its context, assigning to the word the meaning that is most related. The principal advantage of using this algorithm is the ability to deal with inflectional and derivational forms of words, enabling the possibility of bypassing the stemming procedure of words involved in the disambiguation process. Different experiments were performed , with two pa rameters as variables: the context window size , and whether stemming was applied or not. The experimental results (F1 -score) show that our algorithm performs better than the use of the overlap ped metric in the simple Lesk algorithm. Moreover, the experimen ts show that as more information is added to the sense or meaning, and the overlap metric is used, the precision of the simple Lesk algorithm is decreased -in contrast to the performance of our algorithm. |
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