Named Entity Disambiguation using Deep Learning on Graphs
This work addresses entity disambiguation for natural language processing applications, but it is incremental as it applies existing neural techniques to a new dataset.
The paper tackles Named Entity Disambiguation by comparing entities in short sentences with Wikidata graphs, achieving an F1 score of 91.6% on a new dataset using a Bi-LSTM encoding of graph triplets.
We tackle \ac{NED} by comparing entities in short sentences with \wikidata{} graphs. Creating a context vector from graphs through deep learning is a challenging problem that has never been applied to \ac{NED}. Our main contribution is to present an experimental study of recent neural techniques, as well as a discussion about which graph features are most important for the disambiguation task. In addition, a new dataset (\wikidatadisamb{}) is created to allow a clean and scalable evaluation of \ac{NED} with \wikidata{} entries, and to be used as a reference in future research. In the end our results show that a \ac{Bi-LSTM} encoding of the graph triplets performs best, improving upon the baseline models and scoring an \rm{F1} value of $91.6\%$ on the \wikidatadisamb{} test set