CNN-Based Novelty Detection with Effectively Incorporating Document-Level Information
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
With a large number of documents appearing on the web, document-level novelty detection has become important since it can reduce the efforts of finding novel documents by discarding documents sharing redundant information already seen. A recent work proposed a convolutional neural network (CNN)-based novelty detection model with significant performance improvements. We observed that it has a restriction of using document-level information in determining novelty but assumed that the document-level information is more important. As a solution, this paper proposed two methods of effectively incorporating document-level information using a CNN-based novelty detection model. Our methods focus on constructing a feature vector of a target document to be classified by extracting relative information between the target document and source documents given as evidence. A series of experiments showed the superiority of our methods on a standard benchmark collection, TAP-DLND 1.0.
Abstract
With a large number of documents appearing on the web, document-level novelty detection has become important since it can reduce the efforts of finding novel documents by discarding documents sharing redundant information already seen. A recent work proposed a convolutional neural network (CNN)-based novelty detection model with significant performance improvements. We observed that it has a restriction of using document-level information in determining novelty but assumed that the document-level information is more important. As a solution, this paper proposed two methods of effectively incorporating document-level information using a CNN-based novelty detection model. Our methods focus on constructing a feature vector of a target document to be classified by extracting relative information between the target document and source documents given as evidence. A series of experiments showed the superiority of our methods on a standard benchmark collection, TAP-DLND 1.0.
참고문헌 (13)
- Y. Yang, "Topicconditioned novelty detection", Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2002)
- Z. Yi, "Novelty and redundancy detection in adaptive filtering", SIGIR Forum (ACM Special Interest Group on Information Retrieval) (2002)
- T. Ghosal, "Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection", Proc. 27th Int. Conf. Comput. Linguist, pp.2802~2813 (2018)
- C. L. Wayne, "Topic detection and tracking in English and Chinese", Proc. 5th Int. Work. Inf. Retr. with Asian Lang. IRAL 2000, pp.165~172 (2000)
- Y. Kim, "Convolutional Neural Networks for Sentence Classification", Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017, pp.1746~1751 (2014)
- A. Conneau, "Supervised learning of universal sentence representations from natural language inference data", EMNLP 2017 - Conf. Empir. Methods Nat. Lang. Process. Proc, pp.670~680 (2017)
- L. Mou, "Natural language inference by tree-based convolution and heuristic matching", 54th Annu. Meet. Assoc. Comput. Linguist. ACL 2016 - Short Pap, pp.130~136 (2016)
- A Sensitivity Analysis of (and Practitioners’Guide to) Convolutional Neural Networks for Sentence Classification, "B. C. Wallace", ~ (2014)
- Y. Kim, "Character-Aware neural language models", 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp.2741~2749 (2016)
- A. Conneau, "Very deep convolutional networks for text classification", 15th Conf. Eur. Chapter Assoc. Comput. Linguist. EACL 2017- Proc. Conf, 1(2001), pp.1107~1116 (2017)
- T. Ghosal, "TAP-DLND 1. 0 : A corpus for document level novelty detection", Lr. 2018 -11th Int. Conf. Lang. Resour. Eval, 7, pp.3541~3547 (2019)
- M. Karkali, "Using temporal IDF for efficient novelty detection in text streams", 1~30 (2014)
- D. P. Kingma, "Adam : A Method for Stochastic Optimization", 3rd Int. Conf. Learn. Represent. ICLR 2015- Conf. Track Proc, pp.1~15 (2014)