Fine-Grained Entity Typing for Domain Independent Entity Linking
Fine-Grained Entity Typing for Domain Independent Entity Linking
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DOI：
10.1609/aaai.v34i05.6380
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发表时间：
2019-09
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通讯作者：
Yasumasa Onoe;Greg Durrett
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文献类型：
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作者：
Yasumasa Onoe;Greg Durrett
神经实体链接模型非常强大，但是冒着过度适合其训练的领域的风险。对于此问题，“域”不仅是文本流派的特征，由于中立模型通过记住数据集中常见的属性而过度适应。 ，我们的方法模型细粒度的实体属性，可以帮助歧义甚至密切相关的实体。在测试时，我们将其分类为“键入”模型，并使用软类型预测将提及与最相似的候选人实体联系起来。并表明我们的方法优于先前的域无关实体链接系统我们的方法比数据集上的最新神经模型更好地概括了。
Neural entity linking models are very powerful, but run the risk of overfitting to the domain they are trained in. For this problem, a “domain” is characterized not just by genre of text but even by factors as specific as the particular distribution of entities, as neural models tend to overfit by memorizing properties of frequent entities in a dataset. We tackle the problem of building robust entity linking models that generalize effectively and do not rely on labeled entity linking data with a specific entity distribution. Rather than predicting entities directly, our approach models fine-grained entity properties, which can help disambiguate between even closely related entities. We derive a large inventory of types (tens of thousands) from Wikipedia categories, and use hyperlinked mentions in Wikipedia to distantly label data and train an entity typing model. At test time, we classify a mention with this typing model and use soft type predictions to link the mention to the most similar candidate entity. We evaluate our entity linking system on the CoNLL-YAGO dataset (Hoffart et al. 2011) and show that our approach outperforms prior domain-independent entity linking systems. We also test our approach in a harder setting derived from the WikilinksNED dataset (Eshel et al. 2017) where all the mention-entity pairs are unseen during test time. Results indicate that our approach generalizes better than a state-of-the-art neural model on the dataset.