Word Sense Disambiguation using a Bidirectional LSTM

In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations

Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation

Interpretability of a predictive model is a powerful feature that gains the trust of users in the correctness of the predictions. In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images. We present a WSD system that bridges the gap between these two so far disconnected groups of methods. Namely, our system, providing access to several state-of-the-art WSD models, aims to be interpretable as a knowledge-based system while it remains completely unsupervised and knowledge-free. The presented tool features a Web interface for all-word disambiguation of texts that makes the sense predictions human readable by providing interpretable word sense inventories, sense representations, and disambiguation results. We provide a public API, enabling seamless integration.Comment: In Proceedings of the the Conference on Empirical Methods on Natural Language Processing (EMNLP 2017). 2017. Copenhagen, Denmark. Association for Computational Linguistic

BERTを利用した教師あり学習による語義曖昧性解消

Ibaraki UniversityIbaraki UniversityIbaraki UniversityIbaraki UniversityIbaraki University会議名: 言語資源活用ワークショップ2019, 開催地: 国立国語研究所, 会期: 2019年9月2日−4日, 主催: 国立国語研究所 コーパス開発センターBERTはTransformerで利用されるMulti-head attentionを12層（あるいは24層）積み重ねたモデルである。各層のMulti-head attentionは、基本的に、入力単語列に対応する単語埋め込み表現列を出力している。つまりBERTは入力文中の単語に対する埋め込み表現を出力しているが、その埋め込み表現がその単語の文脈に依存した形になっていることが大きな特徴である。この点からBERTから得られる多義語の埋め込み表現を、その多義語の語義曖昧性解消ための特徴ベクトルとして扱えると考えられる。実験では京都大学が公開している日本語版BERT事前学習モデルを利用して、上記の手法をSemEval-2の日本語辞書タスクに対する適用し、高い正解率を得た