Autores
Gelbukh Alexander
Título Scientific Text Entailment and a Textual-Entailment-based framework for cooking domain question answering
Tipo Revista
Sub-tipo JCR
Descripción Sadhana - Academy Proceedings in Engineering Sciences
Resumen Detecting entailment relationship between two sentences has profoundly impacted several different application areas of Natural Language Processing (NLP). Though recognizing textual entailment (TE) is amongst the widely studied problems, the research on detecting entailment between pieces of scientific texts is still in its infancy. To this end the paper discusses implementation of systems based on Long Short-Term Memory (LSTM) neural network and Support Vector Machine (SVM) classifiers using SCITAIL entailment dataset, a dataset in which premise and hypothesis are constituted of scientific texts. Also, a TE-based framework for cooking domain question answering is introduced. The proposed framework exploits the entailment relationship between user question and the cooking questions contained inside a Knowledge Base (KB). © 2021, Indian Academy of Sciences.
Observaciones DOI 10.1007/s12046-021-01557-9
Lugar Nueva Delphi
País India
No. de páginas Article number 24
Vol. / Cap. v. 46 no. 1
Inicio 2021-12-01
Fin
ISBN/ISSN