Abstract
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.
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Notes
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We only consider venue identity rather than similarity because our data set only contains abstract, uninterpretable venue identifiers.
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[7] report similar baseline results with summing instead of averaging over the embeddings of a sequence.
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mpc = 0.5 corresponds to no threshold, as 0.5 is the minimum confidence in a binary classification.
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Acknowledgments
The research described in this paper was conducted in the project SCAD – Scalable Author Name Disambiguation, funded in part by the Leibniz Association (grant SAW-2015-LZI-2), and in part by the Klaus Tschira Foundation. We thank Florian Reitz (dblp) for data preparation and the anonymous TPDL reviewers for their useful suggestions.
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Müller, MC. (2017). Semantic Author Name Disambiguation with Word Embeddings. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2017. Lecture Notes in Computer Science(), vol 10450. Springer, Cham. https://doi.org/10.1007/978-3-319-67008-9_24
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