A Topic Detection Method Based on Word-attention Networks
By:
References
- Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference of Very Large Data Bases. 1215, 487–499.
- Ahn, Y.Y., Bagrow, J.P., & Lehmann, S. (2010). Link communities reveal multiscale complexity in networks. nature, 466(7307), 761–764.
- Asuncion, A., Welling, M., Smyth, P., & Teh, Y.W. (2012). On smoothing and inference for topic models. UAI Press. arXiv:1205.2662.
- Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3, 993–1022.
- Blondel, V.D., Guillaume, J.L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and experiment, 2008(10), P10008.
- Boyack, K.W., Newman, D., Duhon, R.J., Klavans, R., Patek, M., Biberstine, J.R., ... & Börner, K. (2011). Clustering more than two million biomedical publications: Comparing the accuracies of nine text-based similarity approaches. PloS one, 6(3), e18029.
- Cheng, J.P., Dong, L., & Lapata, M. (2016). Long short-term memory-networks for machine reading. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 551–561.
- Doucet, A., & Ahonen-Myka, H. (2010). An efficient any language approach for the integration of phrases in document retrieval. Language resources and evaluation, 44(1), 159–180.
- Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y.N. (2017, July). Convolutional sequence to sequence learning. In International Conference on Machine Learning. 1243–1252.
- Gildea, D., & Jurafsky, D. (2002). Automatic labeling of semantic roles. Computational Linguistics, 28(3), 245–288.
- Girvan, M., & Newman, M.E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.
- Griffiths, T.L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl 1), 5228–5235.
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
- Kalchbrenner, N., et al. Espeholt, L., Simonyan, K., Oord, A.V.D., Graves, A., & Kavukcuoglu, K. (2016). Neural machine translation in linear time. arXiv preprint arXiv:1610.10099.
- Kim, Y., Denton, C., Hoang, L., & Rush, A.M. (2017). Structured attention networks. In International Conference on Learning Representations. arXiv:1702.00887
- Kingsbury, P., & Palmer, M. (2002). From treebank to propbank. Language Resources & Evaluation. Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02).
- Leicht, E.A., & Newman, M.E. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.
- Li, P.J., Lam, W., Bing, L., & Wang, Z. (2017). Deep Recurrent Generative Decoder for Abstractive Text Summarization. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2081–2090.
- McDonald, R., Pereira, F., Kulick, S., Winters, S., Jin, Y., & White, P. (2005, June). Simple algorithms for complex relation extraction with applications to biomedical IE. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL05). 491–498.
- Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009, August). Distant supervision for relation extraction without labeled data. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. 1003–1011.
- Pons, P., & Latapy, M. (2005, October). Computing communities in large networks using random walks. International symposium on computer and information sciences. ISCIS 2005: Computer and Information Sciences - ISCIS 2005, 284–293.
- Ramage, D., Manning, C.D., & Dumais, S. (2011, August). Partially labeled topic models for interpretable text mining. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 457–465. 457–465.
- Schmidhuber, J. (2001). Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term Dependencies. Wiley-IEEE Press.
- Sethy, A., & Ramabhadran, B. (2008). Bag-of-word normalized n-gram models. In Ninth Annual Conference of the International Speech Communication Association. 1594–1597.
- Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423.
- Small, H., Boyack, K.W., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research policy, 43(8), 1450–1467.
- Swampillai, K., & Stevenson, M. (2011, September). Extracting relations within and across sentences. In Proceedings of the International Conference Recent Advances in Natural Language Processing 2011. 25–32.
- Talley, E.M., Newman, D., Mimno, D., Herr, B.W., Wallach, H.M., Burns, G.A., ... & McCallum, A. (2011). Database of NIH grants using machine-learned categories and graphical clustering. Nature Methods, 8(6), 443–444.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems. 5998–6008.
- Velden, T., Boyack, K.W., Gläser, J., Koopman, R., Scharnhorst, A., & Wang, S. (2017). Comparison of topic extraction approaches and their results. Scientometrics, 111(2), 1169–1221.
- Wallach, H.M. (2006, June). Topic modeling: Beyond bag-of-words. In Proceedings of the 23rd international conference on Machine learning, 977–984.
- Yin, W., Schütze, H., Xiang, B., & Zhou, B. (2016). Abcnn: Attention-based convolutional neural network for modeling sentence pairs. Transactions of the Association for Computational Linguistics, 4, 259–272.
- Zeng, D.J., Liu, K., Lai, S., Zhou, G., & Zhao, J. (2014, August). Relation classification via convolutional deep neural network. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 2335–2344.
- Zhang, Y., Lu, J., Liu, F., Liu, Q., Porter, A., Chen, H., & Zhang, G. (2018). Does deep learning help topic extraction? A kernel k-means clustering method with word embedding. Journal of Informetrics, 12(4), 1099–1117.
Language: English
Page range: 139 - 163
Submitted on: Jun 19, 2021
Accepted on: Jul 23, 2021
Published on: Aug 18, 2021
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Related subjects:
© 2021 Zheng Xie, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.