47 citations · 102 across the 10 of their papers we have counts for
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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 +4
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 p…
Representation Learning of Entities and Documents from Knowledge Base Descriptions
Ikuya Yamada, Hiroyuki Shindo, Yoshiyasu Takefuji
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 i…
Studio Ousia's Quiz Bowl Question Answering System
Ikuya Yamada, Ryuji Tamaki, Hiroyuki Shindo +1
In this chapter, we describe our question answering system, which was the winning system at the Human-Computer Question Answering (HCQA) Competition at the Thirty-first Annual Conf…
Learning Distributed Representations of Texts and Entities from Knowledge Base
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda +1
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…
Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda +1
Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia…