Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
2020359 citationsIkuya Yamada, Akari Asai et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Hiroyuki Shindo
Since
Specialization
Citations
This map shows the geographic impact of Hiroyuki Shindo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hiroyuki Shindo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hiroyuki Shindo more than expected).
This network shows the impact of papers produced by Hiroyuki Shindo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hiroyuki Shindo. The network helps show where Hiroyuki Shindo may publish in the future.
Co-authorship network of co-authors of Hiroyuki Shindo
This figure shows the co-authorship network connecting the top 25 collaborators of Hiroyuki Shindo.
A scholar is included among the top collaborators of Hiroyuki Shindo based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Hiroyuki Shindo. Hiroyuki Shindo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yamada, Ikuya & Hiroyuki Shindo. (2019). Pre-training of Deep Contextualized Embeddings of Words and Entities for Named Entity Disambiguation.. arXiv (Cornell University).5 indexed citations
3.
Yamada, Ikuya, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, & Yoshiyasu Takefuji. (2018). Wikipedia2Vec: An Optimized Tool for Learning Embeddings of Words and Entities from Wikipedia.. arXiv (Cornell University).20 indexed citations
Kato, Akihiko, Hiroyuki Shindo, & Yūji Matsumoto. (2018). Construction of Large-scale English Verbal Multiword Expression Annotated Corpus. Language Resources and Evaluation.3 indexed citations
6.
Liu, Jun, et al.. (2018). Automatic Error Correction on Japanese Functional Expressions Using Character-based Neural Machine Translation. Waseda University Repository (Waseda University).2 indexed citations
7.
Shindo, Hiroyuki, et al.. (2018). PDFAnno: a Web-based Linguistic Annotation Tool for PDF Documents. Language Resources and Evaluation.3 indexed citations
8.
Shindo, Hiroyuki, et al.. (2017). Segment-Level Neural Conditional Random Fields for Named Entity Recognition. International Joint Conference on Natural Language Processing. 2. 97–102.15 indexed citations
9.
Shindo, Hiroyuki, et al.. (2017). Coordination Boundary Identification with Similarity and Replaceability. International Joint Conference on Natural Language Processing. 1. 264–272.5 indexed citations
Kato, Akihiko, Hiroyuki Shindo, & Yūji Matsumoto. (2016). Construction of an English Dependency Corpus incorporating Compound Function Words. Language Resources and Evaluation. 1667–1671.4 indexed citations
12.
Shindo, Hiroyuki, et al.. (2016). Multiple Emotions Detection in Conversation Transcripts. Waseda University Repository (Waseda University). 85–94.7 indexed citations
13.
Shindo, Hiroyuki, et al.. (2016). Japanese Text Normalization with Encoder-Decoder Model. International Conference on Computational Linguistics. 129–137.17 indexed citations
14.
Shindo, Hiroyuki, Yusuke Miyao, Akinori Fujino, & Masaaki Nagata. (2012). Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing. Meeting of the Association for Computational Linguistics. 1. 440–448.32 indexed citations
15.
Shindo, Hiroyuki, Akinori Fujino, & Masaaki Nagata. (2011). Insertion Operator for Bayesian Tree Substitution Grammars. Meeting of the Association for Computational Linguistics. 2. 206–211.4 indexed citations
16.
Fujita, Sanae, Kevin Duh, Akinori Fujino, Hirotoshi Taira, & Hiroyuki Shindo. (2010). MSS: Investigating the Effectiveness of Domain Combinations and Topic Features for Word Sense Disambiguation. Meeting of the Association for Computational Linguistics. 383–386.4 indexed citations
17.
Shindo, Hiroyuki, et al.. (2010). Relationships between the vertical distribution of low specific gravity fraction (charred plants) and the soil age or organic C storage in the soil profile of a cumulative Andisol.. 81(2). 112–117.3 indexed citations
18.
Shindo, Hiroyuki, Akinori Fujino, & Masaaki Nagata. (2010). Word Alignment with Synonym Regularization. Meeting of the Association for Computational Linguistics. 137–141.2 indexed citations
19.
Marumoto, Takuya, et al.. (1997). Relationships between the amount of microbial biomass and the physicochemical properties of soil: Comparison betweem volcanic and non-volcanic ash soils.5 indexed citations
20.
Shindo, Hiroyuki, et al.. (1970). Energy expenditure of industrial workers in Japan.. 46. 383–388.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.