Multidocument Keyphrase Extraction Using Recurrent Neural Networks, M.Sc. Thesis Sharif University of Technology ; Sameti, Hossein (Supervisor) ; Bokaei, Mohammad Hadi (Supervisor)
Abstract
Keyphrase extraction, as an important open problem of Natural Language Processing (NLP), is useful as a stand-alone task in the field of Information Extraction and as an upstream task for Information Retrieval, text summarization and classification,etc. In this study, regarding the needs in Persian NLP, artificial neural networks are adopted to extract keyphrases from single documents and a graph-based re-scoring method is proposed for multidocument keyphrase extraction. The proposed method for extracting keyphrases from multiple documents consists of two steps: (1) extracting keyphrases of each document in a cluster using a sequence to sequence model with attention, and (2) re-scoring the...
Cataloging briefMultidocument Keyphrase Extraction Using Recurrent Neural Networks, M.Sc. Thesis Sharif University of Technology ; Sameti, Hossein (Supervisor) ; Bokaei, Mohammad Hadi (Supervisor)
Abstract
Keyphrase extraction, as an important open problem of Natural Language Processing (NLP), is useful as a stand-alone task in the field of Information Extraction and as an upstream task for Information Retrieval, text summarization and classification,etc. In this study, regarding the needs in Persian NLP, artificial neural networks are adopted to extract keyphrases from single documents and a graph-based re-scoring method is proposed for multidocument keyphrase extraction. The proposed method for extracting keyphrases from multiple documents consists of two steps: (1) extracting keyphrases of each document in a cluster using a sequence to sequence model with attention, and (2) re-scoring the...
Find in contentBookmark
|
|