[Retracted] A Multi‐RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency‐Based Scientific Influence Modeling

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.

Predicting the Trending Research Topics by Deep Neural Network Based Content Analysis

“…The study in [14] focuses on semantic consistency of research topics while predicting the research topics. The papers are represented with one hot encoding of high frequency tokens, where each one is considered as a topic.…”

“…In [15], topics are represented with feature vectors where several of the features, such as Web of Science categories, are possibly extracted from external resources. In [14] and [16], the problem modeling differs from our work such that the papers and fields are represented by one hot encoding of the topics and the influence among the fields is used for predicting the topic distributions. On the other hand, in our approach we directly focus on predicting the trend of the keyword by using only the publication history of the venues.…”

When Research Topic Trend Prediction Meets Fact-Based Annotations

“…Especially, considering that recurrent neural network (RNN) and its variants have been well applied in various sequence modeling tasks [16][17][18], Chen et al [19] propose a gated recurrent unit (GRU) based model to predict the trending topics of mutually influenced conferences, which can capture the sequential properties of research evolution in each conference and discover the dependencies among different conferences simultaneously. Xu et al [9,10] follow Chen's work and propose models based on long short-term memory (LSTM) networks to address research topic trend prediction influenced by peer publications. Taheri et al [8] utilize fields of study from the Microsoft Academic to predict the upcoming years' computer science trends based on LSTM.…”

“…Taheri et al [8] utilize fields of study from the Microsoft Academic to predict the upcoming years' computer science trends based on LSTM. However, most existing studies use title words to represent research topics [9,10,14,15,19], which is extremely limited and unconvincing. The reason is that the titles of many papers are very unconventional and even have little correlation with the research topic of the paper.…”

“…The second concentrates on the prediction of the future trend of research topics [6,7]. Especially, inspired by the senior performance of deep learning techniques in modeling sequential information, many corresponding methods are proposed in recent years [8][9][10] to predict the research topic trend.…”