Named Entity Disambiguation for Noisy Text with deep learning

Yotam Eshel, M.Sc. Thesis Seminar
Wednesday, 19.7.2017, 12:00
Taub 601
Prof. Shaul Markovitch

We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model based on GRUs and attention and describe how to train it. We also describe a new way of initializing word and entity embeddings that significantly improves performance. We show our model significantly outperforms existing state-of-the-art methods on WikilinksNED while achieving comparable performance on a smaller newswire dataset.

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