| Revue: | Computación y sistemas |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000410216 |
| ISSN: | 1405-5546 |
| Autores: | Majumder, Goutam1 Pakray, Partha1 Gelbukh, Alexander2 Pinto, David3 |
| Instituciones: | 1National Institute of Technology Mizoram, Mizoram, Aizawl. India 2Instituto Politécnico Nacional, Centro de Investigación en Computación, Ciudad de México. México 3Benemérita Universidad Autónoma de Puebla, Facultad de Ciencias de la Computación, Puebla. México |
| Año: | 2016 |
| Periodo: | Oct-Dic |
| Volumen: | 20 |
| Número: | 4 |
| Paginación: | 647-665 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | Measuring Semantic Textual Similarity (STS), between words/ terms, sentences, paragraph and document plays an important role in computer science and computational linguistic. It also has many applications over several fields such as Biomedical Informatics and Geoinformation. In this paper, we present a survey on different methods of textual similarity and we also reported about the availability of different software and tools those are useful for STS. In natural language processing (NLP), STS is a important component for many tasks such as document summarization, word sense disambiguation, short answer grading, information retrieval and extraction. We split out the measures for semantic similarity into three broad categories such as (i) Topological/Knowledge-based (ii) Statistical/ Corpus Based (iii) String based. More emphasis is given to the methods related to the WordNet taxonomy. Because topological methods, plays an important role to understand intended meaning of an ambiguous word, which is very difficult to process computationally. We also propose a new method for measuring semantic similarity between sentences. This proposed method, uses the advantages of taxonomy methods and merge these information to a language model. It considers the WordNet synsets for lexical relationships between nodes/words and a uni-gram language model is implemented over a large corpus to assign the information content value between the two nodes of different classes |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Procesamiento de datos, Análisis de textos, Procesamiento de lenguaje natural, Similitud semántica, Contenido de información |
| Keyword: | Computer science, Data processing, Text analysis, Natural language processing, Semantic similarity, Information content |
| Texte intégral: | Texto completo (Ver HTML) |
