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(2017) Research and advanced technology for digital libraries, Dordrecht, Springer.
We present a supervised machine learning AND system which tackles semantic similarity between publication titles by means of word embeddings. Word embeddings are integrated as external components, which keeps the model small and efficient, while allowing for easy extensibility and domain adaptation. Initial experiments show that word embeddings can improve the Recall and F score of the binary classification sub-task of AND. Results for the clustering sub-task are less clear, but also promising and overall show the feasibility of the approach.
Publication details
DOI: 10.1007/978-3-319-67008-9_24
Full citation:
Müller, M. (2017)., Semantic author name disambiguation with word embeddings, in J. Kamps, G. Tsakonas, Y. Manolopoulos, L. Iliadis & I. Karydis (eds.), Research and advanced technology for digital libraries, Dordrecht, Springer, pp. 300-311.
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