UBC Entity Linking at TAC-KBP 2013: random forests for high accuracy

Abstract

This paper describe our systems and different runs submitted for the Entity Linking task at TAC-KBP 2013. We developed two systems, one is a generative entity linking model and the other is a supervised system reusing the scores of the previous model using random forests. Our main research interest is Named Entity Disambiguation task and we thus performed a very naive clustering of NIL instances. In fact, our best run scores at par to the best system on accuracy (ignoring NIL clustering), with another run we obtain top performance on KB mentions, both in accuracy and B-cubed F1.

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Cite this paper

@inproceedings{Barrena2013UBCEL, title={UBC Entity Linking at TAC-KBP 2013: random forests for high accuracy}, author={Ander Barrena and Eneko Agirre and Aitor Soroa}, booktitle={TAC}, year={2013} }