Yoshiyasu Takefuji


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Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia
Ikuya Yamada | Akari Asai | Jin Sakuma | Hiroyuki Shindo | Hideaki Takeda | Yoshiyasu Takefuji | Yuji Matsumoto
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The embeddings of entities in a large knowledge base (e.g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge. In this paper, we present Wikipedia2Vec, a Python-based open-source tool for learning the embeddings of words and entities from Wikipedia. The proposed tool enables users to learn the embeddings efficiently by issuing a single command with a Wikipedia dump file as an argument. We also introduce a web-based demonstration of our tool that allows users to visualize and explore the learned embeddings. In our experiments, our tool achieved a state-of-the-art result on the KORE entity relatedness dataset, and competitive results on various standard benchmark datasets. Furthermore, our tool has been used as a key component in various recent studies. We publicize the source code, demonstration, and the pretrained embeddings for 12 languages at https://wikipedia2vec.github.io/.


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Representation Learning of Entities and Documents from Knowledge Base Descriptions
Ikuya Yamada | Hiroyuki Shindo | Yoshiyasu Takefuji
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.


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Learning Distributed Representations of Texts and Entities from Knowledge Base
Ikuya Yamada | Hiroyuki Shindo | Hideaki Takeda | Yoshiyasu Takefuji
Transactions of the Association for Computational Linguistics, Volume 5

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.


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Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
Ikuya Yamada | Hiroyuki Shindo | Hideaki Takeda | Yoshiyasu Takefuji
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning


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Enhancing Named Entity Recognition in Twitter Messages Using Entity Linking
Ikuya Yamada | Hideaki Takeda | Yoshiyasu Takefuji
Proceedings of the Workshop on Noisy User-generated Text