Neural-DINF: A Neural Network based Framework for Measuring Document Influence

Jie Tan, Ying Li

Abstract:

Measuring the scholarly impact of a document
without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the
word frequency change. In this paper, we use
both frequency changes and word semantic
shifts to measure document influence by developing a neural network based framework. Our
model has three steps. Firstly, we train word
embeddings for different time periods. Subsequently, we propose an unsupervised method
to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our
model outperforms DIM.