@inproceedings{19bfdcf677f74b31a8db4195a1f3d9e5,
title = "Interactive area topics extraction with policy gradient",
abstract = "Extracting representative topics and improving the extraction performance is rather challenging. In this work, we formulate a novel problem, called Interactive Area Topics Extraction, and propose a learning interactive topics extraction (LITE) model to regard this problem as a sequential decision making process and construct an end-to-end framework to use interaction with users. In particular, we use recurrent neural network (RNN) decoder to address the problem and policy gradient method to tune the model parameters considering user feedback. Experimental result has shown the effectiveness of the proposed framework.",
keywords = "Interactive area topics extraction, Policy gradient, RNN decoder",
author = "Jingfei Han and Wenge Rong and Fang Zhang and Yutao Zhang and Jie Tang and Zhang Xiong",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 27th International Conference on Artificial Neural Networks, ICANN 2018 ; Conference date: 04-10-2018 Through 07-10-2018",
year = "2018",
doi = "10.1007/978-3-030-01424-7\_9",
language = "英语",
isbn = "9783030014230",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "84--93",
editor = "Yannis Manolopoulos and Barbara Hammer and Vera Kurkova and Lazaros Iliadis and Ilias Maglogiannis",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings",
address = "德国",
}