47 citations · 228 across the 17 of their papers we have counts for
14 papers · 1 filter
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi +9
Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity m…
NAIST Academic Travelogue Dataset
Hiroki Ouchi, Hiroyuki Shindo, Shoko Wakamiya +5
We have constructed NAIST Academic Travelogue Dataset (ATD) and released it free of charge for academic research. This dataset is a Japanese text dataset with a total of over 31 mi…
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
Ikuya Yamada, Akari Asai, Hiroyuki Shindo +2
Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based…
Length-controllable Abstractive Summarization by Guiding with Summary Prototype
Itsumi Saito, Kyosuke Nishida, Kosuke Nishida +5
We propose a new length-controllable abstractive summarization model. Recent state-of-the-art abstractive summarization models based on encoder-decoder models generate only one sum…
Neural Attentive Bag-of-Entities Model for Text Classification
Ikuya Yamada, Hiroyuki Shindo
This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide…
Global Entity Disambiguation with BERT
Ikuya Yamada, Koki Washio, Hiroyuki Shindo +1
We propose a global entity disambiguation (ED) model based on BERT. To capture global contextual information for ED, our model treats not only words but also entities as input toke…
Improving Multi-Word Entity Recognition for Biomedical Texts
Hamada A. Nayel, H. L. Shashirekha, Hiroyuki Shindo +1
Biomedical Named Entity Recognition (BioNER) is a crucial step for analyzing Biomedical texts, which aims at extracting biomedical named entities from a given text. Different super…
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…
A Span Selection Model for Semantic Role Labeling
Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto
We present a simple and accurate span-based model for semantic role labeling (SRL). Our model directly takes into account all possible argument spans and scores them for each label…
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…
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples
Ikuya Yamada, Motoki Sato, Hiroyuki Shindo
This paper describes our approach for the triple scoring task at the WSDM Cup 2017. The task required participants to assign a relevance score for each pair of entities and their t…
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…