Abstract
We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.
Highlights
Methods capable of learning distributed representations of arbitrary-length texts, such as sentences and paragraphs, have recently attracted considerable attention (Le and Mikolov, 2014; Kiros et al, 2015; Li et al, 2015; Wieting et al, 2016; Hill et al, 2016b; Kenter et al, 2016)
Our Neural Text-Entity Encoder (NTEE) models were able to outperform the state-of-the-art models in all datasets in terms of Pearson’s r
We tested the performance of the NTEE model without using the string similarity features and found that these features contributed to the performance
Summary
Methods capable of learning distributed representations of arbitrary-length texts (i.e., fixed-length continuous vectors that encode the semantics of texts), such as sentences and paragraphs, have recently attracted considerable attention (Le and Mikolov, 2014; Kiros et al, 2015; Li et al, 2015; Wieting et al, 2016; Hill et al, 2016b; Kenter et al, 2016) These methods aim to learn generic representations that are useful across domains similar to word embedding methods such as Word2vec (Mikolov et al, 2013b) and GloVe (Pennington et al, 2014). 1https://github.com/studio-ousia/ntee base (KB) such as Wikipedia and Freebase These methods encode information of entities in the KB into a continuous vector space. We use humanedited entity annotations obtained from Wikipedia (see Table 1) as supervised data of relevant entities to the texts containing these annotations.
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