Abstract
Computer science discipline includes many research fields, which mutually influence and promote each other's development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.
Highlights
In recent years, with the rapid development of computer science and technology, the number of papers in many research fields of computer science discipline has been increasing rapidly. ese research fields influence each other and promote their own development [1]
We abstract the papers from five fields: Computation and Language (CL), Computer Vision and Pattern Recognition (CV), Machine Learning (ML), Information Retrieval (IR), and Artificial Intelligence (AI). e title of a paper can best reflect the topic of a paper
To further validate the effectiveness of SASC, we make comparisons with variants of SASC as follows: (1) SASC without spatial attention (SASC-SA): to evaluate the effect of multi-Recurrent Neural Network (RNN) field topic representation based on semantic consistency-based scientific influence modeling on model performance, we evaluate the performance of a variant of SASC that does not use spatial attention when predicting research topics
Summary
With the rapid development of computer science and technology, the number of papers in many research fields of computer science discipline has been increasing rapidly. ese research fields influence each other and promote their own development [1]. With the rapid development of computer science and technology, the number of papers in many research fields of computer science discipline has been increasing rapidly. Tracking the research progress and predicting the research topic trend of these research fields are of great significance. It has important reference value for scientific and technological innovation decision-making [2] and helps to guide government agencies to formulate scientific development strategies and policies. It is of great significance for researchers to keep up with the rapid development of research [3]. Soumya et al propose an effective method to discover the development trend of science by using graphbased subject classification of academic publications [8]
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