Language Model for Assessing the Author Similarity
Jiayu Chen, Junli Wang   
Journal of Information Technology Review, Vol. 7, No. 2, 2016.
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Abstract:
Currently, it is crucial for researchers to know if others have similar research objective. Nevertheless, the identification of authors sharing the same motivations and interests may be complex, especially as the amount of research publications is growing rapidly.  Furthermore, information about research paper is often fragmented and incomplete. The incomplete information, or metadata, in this paper refers to abstracts, keywords, journals, organizations and so on. Thus, this paper analyzes the metadata information relative to an author, in order to find out similar authors. Author Similarity Model, a novel language model which is evolved from Author Topic Model, has been developed in this paper. For author similarity modeling, a four-dimensional vector has been set up to describe every author. Therefore authorâ€™s neighbors (a group of people who have the same direction of research) can be found out by calculating similarity between vectors.