Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
| Autor: | Zhiqian Chen, Kaiqun Fu, Taoran Ji, Nathan Self, Naren Ramakrishnan, Chang-Tien Lu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Computer Science - Machine Learning Dependency (UML) Computer science Emerging technologies Machine Learning (stat.ML) ComputingMilieux_LEGALASPECTSOFCOMPUTING 02 engineering and technology computer.software_genre Point process Machine Learning (cs.LG) Task (project management) 020901 industrial engineering & automation Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Digital Libraries (cs.DL) GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries) Computer Science - Digital Libraries ComputingMilieux_GENERAL Recurrent neural network Dynamics (music) 020201 artificial intelligence & image processing Data mining Timestamp Citation computer |
| Zdroj: | IJCAI |
| Popis: | Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting. |
| Databáze: | OpenAIRE |
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