Fine-Grained Classification of Researcher-Related Web Pages Using String and Contextual Features

Profiling a researcher's expertise and interests is crucial for building an expert recommendation system in the academic domain. Conventional methods have used bibliographic databases to obtain textual data regarding a researcher. However, information about academic activities beyond writing academic papers is often scattered across various web pages. To enrich the information sources of researcher profiling, this paper presents a novel task, fine-grained classification of researcher-related web pages obtained using a search query comprising a full name and an affiliation string. Our method fuses two neural networks based on pre-trained embedding models to extract string and contextual features from the URL and page text, respectively. Experiments conducted using a dataset of 477 Japanese researchers demonstrated the effectiveness of the proposed method compared to baseline and conventional methods.