(National Science Library, Chinese Academy of Sciences, Beijing 100190, China);(Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China)
[Objective] This paper tries to identify semantics similar to the novelty points from preliminary searching results, aiming to retrieve needed journal articles or patents automatically. [Methods] First, we designed a deep multi-task hierarchical classification model based on Bi-GRU-ATT. Then, we trained several different hierarchical classification models using International Patent Classification Table (IPC) categories and patents. Third, we used a small amount of paper data to fine-tune the model for papers and patents. Finally, we compared the semantic categories of new points and candidate records to collect the matching ones. [Results] With two-level classification of patents under IPC (E21B), the new model’s precisions were 82.37% and 73.55% respectively, which were better than the benchmark models. For real novelty search points data, the precision of semantic matching was 88.13%, which was 15.16% higher than that of TF-IDF. [Limitations] Only examined our model with a small amount of IPC categories . [Conclusions] The proposed method improves the semantic matching of novelty search points.
(Li Fengxia, Zhan Yuhua, Zhao Junping, et al.Design and Practice of Tsinghua University Sci-Tech Novelty Search System[J]. Journal of Academic Libraries, 2014, 32(2): 33-38.)
(Wang Peixia, Yu Hai, Chen Li, et al.Using Intelligent System to Extract Search Terms for Sci-Tech Novelty Retrieval[J]. New Technology of Library and Information Service, 2016(11): 82-93.)
(Wang Zixuan, Le Xiaoqiu, He Yuanbiao.Recognizing Core Topic Sentences with Improved TextRank Algorithm Based on WMD Semantic Similarity[J]. Data Analysis and Knowledge Discovery, 2017, 1(4): 5-12.)
[4]
Kusner M J, Sun Y, Kolkin N I, et al.From Word Embeddings to Document Distances[C]// Proceedings of the 32nd International Conference on International Conference on Machine Learning. 2015: 957-966.
[5]
Huang P S, He X, Gao J, et al.Learning Deep Structured Semantic Models for Web Search Using Clickthrough Data[C]// Proceedings of the 22nd ACM International Conference on Information & Knowledge Management. 2013: 2333-2338.
(Li Xin, Wang Jingjing, Yang Zi, et al.Identifying Emerging Technologies Based on Subject-Action-Object[J]. Journal of Intelligence, 2016, 35(3): 80-84.)
(He Xijun, Ma Shan, Wu Yuying.Research on Semantic Matching for Online Technology Supply and Demand Information Based on Ontology and SAO Structure[J]. Information Science, 2018, 36(11): 95-100.)
[8]
Joulin A, Grave E, Bojanowski P, et al.Bag of Tricks for Efficient Text Classification[OL]. arXiv Preprint, arXiv: 1607.01759.
[9]
Kim Y.Convolutional Neural Networks for Sentence Classification[OL]. arXiv Preprint, arXiv: 1408.5882.
[10]
Li F, Zhang M, Fu G, et al.A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features[OL]. arXiv Preprint, arXiv: 1608.07720.
[11]
Pappas N, Popescu-Belis A.Multilingual Hierarchical Attention Networks for Document Classification[OL]. arXiv Preprint, arXiv: 1707.00896.
[12]
Misra I, Shrivastava A, Gupta A, et al.Cross-Stitch Networks for Multi-task Learning[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016: 3994-4003.
[13]
Mikolov T, Sutskever I, Chen K, et al.Distributed Representations of Words and Phrases and Their Compositionality[J]. Advances in Neural Information Processing Systems, 2013, 26: 3111-3119.
[14]
Cho K, Van Merrienboer B, Gulcehre C, et al.Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation[OL]. arXiv Preprint, arXiv: 1406.1078.
[15]
Raffel C, Ellis D P W. Feed-Forward Networks with Attention can Solve Some Long-Term Memory Problems[OL]. arXiv Preprint, arXiv: 1512.08756.
[16]
Howard J, Ruder S.Universal Language Model Fine-tuning for Text Classification[OL]. arXiv Preprint, arXiv: 1801.06146.