35 citations · 59 across the 2 of their papers we have counts for
12 papers
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…
Gated Graph Recursive Neural Networks for Molecular Property Prediction
Hiroyuki Shindo, Yuji Matsumoto
Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT)…
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…