Search Results for author: Hiroyuki Shindo

Found 45 papers, 15 papers with code

Arukikata Travelogue Dataset

no code implementations19 May 2023 Hiroki Ouchi, Hiroyuki Shindo, Shoko Wakamiya, Yuki Matsuda, Naoya Inoue, Shohei Higashiyama, Satoshi Nakamura, Taro Watanabe

We have constructed Arukikata Travelogue Dataset and released it free of charge for academic research.

Improved Decomposition Strategy for Joint Entity and Relation Extraction

no code implementations Journal of Natural Language Processing 2021 Van-Hien Tran, Van-Thuy Phi, Akihiko Kato, Hiroyuki Shindo, Taro Watanabe, Yuji Matsumoto

A recent study (Yu et al. 2020) proposed a novel decomposition strategy that splits the task into two interrelated subtasks: detection of the head-entity (HE) and identification of the corresponding tail-entity and relation (TER) for each extracted head-entity.

Joint Entity and Relation Extraction Relation +1

Length-controllable Abstractive Summarization by Guiding with Summary Prototype

no code implementations21 Jan 2020 Itsumi Saito, Kyosuke Nishida, Kosuke Nishida, Atsushi Otsuka, Hisako Asano, Junji Tomita, Hiroyuki Shindo, Yuji Matsumoto

Unlike the previous models, our length-controllable abstractive summarization model incorporates a word-level extractive module in the encoder-decoder model instead of length embeddings.

Abstractive Text Summarization

Neural Attentive Bag-of-Entities Model for Text Classification

3 code implementations CONLL 2019 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.

General Classification Question Answering +1

Gated Graph Recursive Neural Networks for Molecular Property Prediction

no code implementations31 Aug 2019 Hiroyuki Shindo, Yuji Matsumoto

Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science.

Drug Discovery Molecular Property Prediction +1

Stochastic Tokenization with a Language Model for Neural Text Classification

no code implementations ACL 2019 Tatsuya Hiraoka, Hiroyuki Shindo, Yuji Matsumoto

To make the model robust against infrequent tokens, we sampled segmentation for each sentence stochastically during training, which resulted in improved performance of text classification.

General Classification Language Modelling +5

Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia

no code implementations EMNLP 2020 Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto

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.

World Knowledge

Cooperating Tools for MWE Lexicon Management and Corpus Annotation

no code implementations COLING 2018 Yuji Matsumoto, Akihiko Kato, Hiroyuki Shindo, Toshio Morita

Those two tools cooperate so that the words and multi-word expressions stored in Cradle are directly referred to by ChaKi in conducting corpus annotation, and the words and expressions annotated in ChaKi can be output as a list of lexical entities that are to be stored in Cradle.

Management POS

Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners

no code implementations ACL 2018 Jun Liu, Hiroyuki Shindo, Yuji Matsumoto

We present a computer-assisted learning system, Jastudy, which is particularly designed for Chinese-speaking learners of Japanese as a second language (JSL) to learn Japanese functional expressions with suggestion of appropriate example sentences.

Clustering Sentence

Representation Learning of Entities and Documents from Knowledge Base Descriptions

2 code implementations COLING 2018 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).

Entity Typing General Classification +3

Interpretable Adversarial Perturbation in Input Embedding Space for Text

2 code implementations8 May 2018 Motoki Sato, Jun Suzuki, Hiroyuki Shindo, Yuji Matsumoto

This paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space.

Sentence

Studio Ousia's Quiz Bowl Question Answering System

no code implementations23 Mar 2018 Ikuya Yamada, Ryuji Tamaki, Hiroyuki Shindo, Yoshiyasu Takefuji

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 Conference on Neural Information Processing Systems (NIPS).

BIG-bench Machine Learning Information Retrieval +2

Segment-Level Neural Conditional Random Fields for Named Entity Recognition

no code implementations IJCNLP 2017 Motoki Sato, Hiroyuki Shindo, Ikuya Yamada, Yuji Matsumoto

We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking.

Chunking Morphological Tagging +3

Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information

no code implementations CONLL 2017 Go Inoue, Hiroyuki Shindo, Yuji Matsumoto

One reason for this is that in the tagging scheme for such languages, a complete POS tag is formed by combining tags from multiple tag sets defined for each morphosyntactic category.

Multi-Task Learning Part-Of-Speech Tagging +4

English Multiword Expression-aware Dependency Parsing Including Named Entities

no code implementations ACL 2017 Akihiko Kato, Hiroyuki Shindo, Yuji Matsumoto

Because syntactic structures and spans of multiword expressions (MWEs) are independently annotated in many English syntactic corpora, they are generally inconsistent with respect to one another, which is harmful to the implementation of an aggregate system.

Dependency Parsing

Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis

no code implementations ACL 2017 Hiroki Ouchi, Hiroyuki Shindo, Yuji Matsumoto

The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates.

Sentence

Construction of an English Dependency Corpus incorporating Compound Function Words

no code implementations LREC 2016 Akihiko Kato, Hiroyuki Shindo, Yuji Matsumoto

Nevertheless, this method often leads to the following problem: A node derived from an MWE could have multiple heads and the whole dependency structure including MWE might be cyclic.

Constituency Parsing Dependency Parsing +2

Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

1 code implementation CONLL 2016 Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji

The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words.

Entity Disambiguation Entity Linking

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