Papers by Hiroyuki Shindo
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation (2024.findings-eacl)
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Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, Taro Watanabe
| Challenge: | et al., 2006) considers geographic relatedness among geo-entity mentions in document-level geoparsing. |
| Approach: | They present a Japanese travelogue dataset that considers geographic relatedness among geo-entity mentions. |
| Outcome: | The proposed dataset includes 200 travelogue documents with rich geo-entity information . it shows that human activities, mobility, and events are often described with natural language expressions of locations or geographic entities (geo-entities) |
A Span Selection Model for Semantic Role Labeling (D18-1)
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| Challenge: | Existing models for semantic role labeling use BIO tags to predict argument spans . but performance of these approaches is weak . |
| Approach: | They propose a span-based model that takes into account all possible argument spans and scores them for each label. |
| Outcome: | The proposed model achieves state-of-the-art results on the CoNLL-2005 and 2012 datasets. |
Decomposed Local Models for Coordinate Structure Parsing (N19-1)
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| Challenge: | Existing methods for coordination boundary identification are inefficient, even for humans. |
| Approach: | They propose a simple and accurate model for coordination boundary identification . they combine syntactic parsers and neural networks to compute similarity and replaceability features of conjuncts . |
| Outcome: | The proposed model outperforms similarity-based approaches but cannot handle more than two conjuncts in a coordination and multiple coordinations at once. |
Automatic Generation of a Compositional QA Benchmark for Geospatial Reasoning under Spatial and Entity Constraints (2026.eacl-srw)
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| Challenge: | Recent advances in large language models have enhanced their ability to perform reasoning tasks that integrate linguistic, visual, and factual information. |
| Approach: | They propose a method for constructing compositional geographic question answering datasets that jointly consider spatial and entity constraints. |
| Outcome: | The proposed method performs well on questions involving rich entity grounding, but its accuracy drops on quantitative spatial reasoning questions. |
Chemical Compounds Knowledge Visualization with Natural Language Processing and Linked Data (L18-1)
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| Challenge: | Existing systems for chemical compounds extraction and registration depend on human labor . CAS databases are being created, but information written in other languages is not exploited well . |
| Approach: | They propose a visualization system for chemical compounds extracted from Japanese texts and chemical compound databases represented as Linked Data (LD) system integrates extracted results with existing chemical compound knowledge to provide different views of chemical compounds. |
| Outcome: | The proposed system integrates extraction results with existing chemical compound knowledge to provide different views of chemical compounds. |
Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia (2020.emnlp-demos)
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Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto
| Challenge: | Existing tools for learning the embeddings of words and entities from Wikipedia are not yet available. |
| Approach: | They propose a Python-based tool for learning Wikipedia embeddings from Wikipedia . they use a Wikipedia dump file as an argument to issue a single command . |
| Outcome: | The proposed tool achieves state-of-the-art results on the KORE entity relatedness dataset and competitive results on benchmark datasets. |
Graph-Structured Trajectory Extraction from Travelogues (2025.acl-long)
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Aitaro Yamamoto, Hiroyuki Otomo, Hiroki Ouchi, Shohei Higashiyama, Hiroki Teranishi, Hiroyuki Shindo, Taro Watanabe
| Challenge: | Existing studies treat travelogues as sequences of visited locations, but they lack a benchmark dataset. |
| Approach: | They propose to represent the trajectory as a graph that can capture the hierarchy as well as the visiting order and construct a benchmark dataset for the extraction. |
| Outcome: | The proposed dataset shows that even naive baseline systems can predict visited locations and the visiting order between them, while it is more challenging to predict the hierarchical relations. |
Representation Learning of Entities and Documents from Knowledge Base Descriptions (C18-1)
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| Challenge: | Using documents extracted from Wikipedia, we train a neural network model that learns distributed representations of entities and documents directly from a knowledge base. |
| Approach: | They propose a neural network model that learns distributed representations of entities from a knowledge base. |
| Outcome: | The proposed model performs state-of-the-art on fine-grained entity typing and multiclass text classification tasks. |
Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path (2021.acl-long)
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| Challenge: | Existing methods for named entity recognition assume entities are not nested within other entities, so-called flat NER. |
| Approach: | They propose a layered method for nested named entity recognition . they use a set of hidden states to exclude the influence of the best path . |
| Outcome: | The proposed method performs better on ACE2004, ACE2005, and GENIA datasets. |
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN (N19-1)
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| Challenge: | Recent work on relation classification has gained much success by exploiting deep neural networks. |
| Approach: | They propose a relation classification model using Segment-level Attention-based Convolutional Neural Networks and Dependency-based Recurrent Neural networks. |
| Outcome: | The proposed model is comparable to the state-of-the-art without external lexical features on the SemEval-2010 dataset. |
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention (2020.emnlp-main)
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| Challenge: | Existing models for entity representations do not capture information in a knowledge base, and cannot represent entities that do not exist in the KB. |
| Approach: | They propose a pretrained contextualized representation of words and entities based on the bidirectional transformer. |
| Outcome: | The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. |
Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners (P18-4)
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| Challenge: | a large number of Chinese characters are commonly used both in Chinese and Japanese. |
| Approach: | They propose a computer-assisted learning system for Chinese-speaking learners of Japanese as a second language (JSL) they use a free Japanese morphological analyzer MeCab to learn Japanese functional expressions with suggestion of appropriate example sentences. |
| Outcome: | The proposed system automatically recognizes Japanese functional expressions using a free Japanese morphological analyzer and is retrained on a new conditional random field model. |
Stochastic Tokenization with a Language Model for Neural Text Classification (P19-1)
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| Challenge: | Sentences segmented with words or subwords can be difficult to perform text classification tasks. |
| Approach: | They propose a method to learn tokenization and text classification simultaneously to address these problems. |
| Outcome: | The proposed method improves on sentiment analysis in Japanese and Chinese using tokenization and text classification models. |
Structured Refinement for Sequential Labeling (2021.findings-acl)
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| Challenge: | Existing work on identifying target-irrelevant information relies on locally normalized attention without considering possible labels at other time steps. |
| Approach: | They propose to extend local normalized attention to leverage structural information for refinement . they propose to use two implementation tricks to accelerate CRF computation and an initialization trick for Chinese character embeddings . |
| Outcome: | The proposed method can be extended to include Chinese character embeddings and two implementation tricks to accelerate CRF computation. |
Global Entity Disambiguation with BERT (2022.naacl-main)
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| Challenge: | Entity disambiguation (ED) is a task of assigning mentions to referent entities in a knowledge base. |
| Approach: | They propose a global entity disambiguation (ED) model based on BERT . they train the model using a large entity-annotated corpus obtained from Wikipedia . |
| Outcome: | The proposed model can disambiguate masked entities based on words and non-masked ones at the inference time. |
Construction of Large-scale English Verbal Multiword Expression Annotated Corpus (L18-1)
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| Challenge: | In this paper, we focus on verbal MWEs, whose accurate recognition is challenging because they could be discontinuous. |
| Approach: | They conduct large-scale annotations of VMWEs on the Wall Street Journal portion of Ontonotes . they first construct a VMwe dictionary based on the english-language Wiktionary . |
| Outcome: | The proposed resource annotates 7,833 VMWE instances belonging to various categories . the authors hope the results will help to develop models for MWE recognition and dependency parsing . |
PDFAnno: a Web-based Linguistic Annotation Tool for PDF Documents (L18-1)
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| Challenge: | Currently, linguistic annotation tools for PDF documents focus on plain-text documents. |
| Approach: | They propose a web-based linguistic annotation tool for PDF documents . it offers functions for various types of linguistic annotations directly on PDF . |
| Outcome: | The proposed tool can annotate on PDF documents with named entity, dependency relation, and coreference chain. |
Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation (2020.coling-main)
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Yuya Sawada, Takashi Wada, Takayoshi Shibahara, Hiroki Teranishi, Shuhei Kondo, Hiroyuki Shindo, Taro Watanabe, Yuji Matsumoto
| Challenge: | a new method for nominal coordination boundary identification is proposed . it uses pre-trained word embeddings to measure similarities of words and detects the span of coordination . |
| Approach: | They propose a method for nominal coordination boundary identification that uses pre-trained word embeddings to measure similarities of words and detects the span of coordination. |
| Outcome: | The proposed method can identify coordination boundaries without training on labeled data . it is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases. |