Yotam Eshel, M.Sc. Thesis Seminar
Wednesday, 19.7.2017, 12:00
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.