| Revue: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560443 |
| ISSN: | 1405-5546 |
| Autores: | Shashavali, D1 Vishwjeet, V1 Kumar, Rahul1 Mathur, Gaurav1 Nihal, Nikhil1 Mukherjee, Siddhartha1 Patil, Suresh Venkanagouda1 |
| Instituciones: | 1Samsung R & D Bangalore, Bangalore. India |
| Año: | 2019 |
| Periodo: | Jul-Sep |
| Volumen: | 23 |
| Número: | 3 |
| Paginación: | 999-1004 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | In goal-oriented conversational agents like Chatbots, finding the similarity between user input and representative text result is a big challenge. Generally, the conversational agent developers tend to provide a minimal number of utterances per intent, which makes the classification task difficult. The problem becomes more complex when the length of the representative text per action is short and the length of the user input is long. We propose a methodology that derives Sentence Similarity score based on N-gram and Sliding Window and uses the FastText Word Embeddings technique which outperforms the current state-of-the-art Sentence Similarity results. We are also publishing a dataset on the shopping domain, to build conversational agents. And the extensive experiments done on the dataset fetched better results in accuracy, precision and recall by 6%, 2% and 80% respectively. It also evinces that our solution generalizes well on the low corpus and requires no training. |
| Disciplinas: | Ciencias de la computación |
| Palabras clave: | Inteligencia artificial |
| Keyword: | Sentence similarity, Word embeddings, Natural language processing, Sliding window, N-grams, Text classification, Artificial intelligence |
| Texte intégral: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
