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ORCIDYo Ehara
Person information
- affiliation: Tokyo Gakugei University, Japan
- unicode name: 江原 遥
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2020 – today
- 2023
[c35]Yo Ehara:
A Writing Support System that Scaffolds Language Learners via Autocompletion with Difficulty Prediction. AI-GEL@AIED 2023: 20-24- [c34]Yo Ehara:
Innovative Software to Efficiently Learn English Through Extensive Reading and Personalized Vocabulary Acquisition. AIED (Posters/Late Breaking Results/...) 2023: 187-192 - [c33]Yo Ehara:
A Support System to Help Teachers Design Course Plans Conforming to National Curriculum Guidelines. AIED (Posters/Late Breaking Results/...) 2023: 549-554 - [c32]Yo Ehara:
Course Concepts: How Readable Are They for ESL Learners? EDM 2023 - [c31]Yo Ehara:
Measuring Similarity between Manual Course Concepts and ChatGPT-generated Course Concepts. EDM 2023 - [c30]Yo Ehara
:
Analyzing Readability of Academic Paper Abstracts for ESL Learners across Various Computer Science Subfields. SIGCSE (2) 2023: 1296 - 2022
- [j3]Yo Ehara:
Uncertainty-aware Personalized Readability Assessment Framework for Second Language Learners. J. Inf. Process. 30: 352-360 (2022) - [c29]Yo Ehara:
An Intelligent Interactive Support System for Word Usage Learning in Second Languages. AIED (1) 2022: 453-464 - [c28]Yo Ehara:
Assessing Readability of Learning Materials on Artificial Intelligence in English for Second Language Learners. AIED (2) 2022: 475-478 - [c27]Yo Ehara:
Distributional Estimation of Personalized Second-Language Vocabulary Sizes with Wordlists that the Learner is Likely to Know. AIED (2) 2022: 537-541 - [c26]Yo Ehara:
Predicting Second Language Learners' Actual Knowledge Using Self-perceived Knowledge. AIED (1) 2022: 704-710 - [c25]Yo Ehara:
No Meaning Left Unlearned: Predicting Learners' Knowledge of Atypical Meanings of Words from Vocabulary Tests for Their Typical Meanings. EDM 2022 - [c24]Yo Ehara:
Selecting Reading Texts Suitable for Incidental Vocabulary Learning by Considering the Estimated Distribution of Acquired Vocabulary. EDM 2022 - [c23]Yo Ehara:
Analyzing Semantically Distinctive English Word Usages in Computer Science for English-as-a-Second-Language Learners. ITiCSE (2) 2022: 636 - 2021
- [c22]Yo Ehara:
To What Extent Does Lexical Normalization Help English-as-a-Second Language Learners to Read Noisy English Texts? W-NUT 2021: 451-456 - [c21]Yo Ehara:
Evaluation of Unsupervised Automatic Readability Assessors Using Rank Correlations. Eval4NLP 2021: 62-72 - [c20]Yo Ehara:
LURAT: a Lightweight Unsupervised Automatic Readability Assessment Toolkit for Second Language Learners. ICTAI 2021: 806-814 - [c19]Yo Ehara:
Readability and Linearity. PACLIC 2021: 503-512 - 2020
- [c18]Yo Ehara:
Supervised Visualization of Vocabulary Knowledge towards Explainable Support of Second Language Learners. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering (1) 2020 - [c17]Yo Ehara:
Interpreting Neural CWI Classifiers' Weights as Vocabulary Size. BEA@ACL 2020: 171-176
2010 – 2019
- 2019
- [c16]Yo Ehara:
Analyzing Similarities between Word Frequency Distributions of Various Corpora to Find Effective Features for Complex Word Identification. ICDM Workshops 2019: 1-4 - [c15]Yo Ehara:
Uncertainty-Aware Personalized Readability Assessments for Second Language Learners. ICMLA 2019: 1909-1916 - [c14]Yo Ehara:
Graph-Based Analysis of Similarities between Word Frequency Distributions of Various Corpora for Complex Word Identification. ICMLA 2019: 1982-1986 - [c13]Yo Ehara:
Neural Rasch Model: How Do Word Embeddings Adjust Word Difficulty? PACLING 2019: 88-96 - 2018
- [j2]Yo Ehara, Issei Sato, Hidekazu Oiwa, Hiroshi Nakagawa:
Mining Words in the Minds of Second Language Learners for Learner-specific Word Difficulty. J. Inf. Process. 26: 267-275 (2018) - [c12]Yo Ehara:
Building an English Vocabulary Knowledge Dataset of Japanese English-as-a-Second-Language Learners Using Crowdsourcing. LREC 2018 - 2017
- [c11]Yo Ehara:
Language-Independent Prediction of Psycholinguistic Properties of Words. IJCNLP(2) 2017: 330-336 - 2016
- [c10]Natsuda Laokulrat, Sang Phan, Noriki Nishida, Raphael Shu, Yo Ehara, Naoaki Okazaki, Yusuke Miyao, Hideki Nakayama:
Generating Video Description using Sequence-to-sequence Model with Temporal Attention. COLING 2016: 44-52 - [c9]Masaki Kanno, Yo Ehara, Masaharu Hirota, Shohei Yokoyama, Hiroshi Ishikawa:
Visualizing High-Risk Paths using Geo-tagged Social Data for Disaster Mitigation. LBSN@SIGSPATIAL/GIS 2016: 4:1-4:8 - [c8]Yo Ehara, Yukino Baba, Masao Utiyama, Eiichiro Sumita:
Assessing Translation Ability through Vocabulary Ability Assessment. IJCAI 2016: 3712-3718 - [c7]Yuya Nozawa, Masaki Endo, Yo Ehara, Masaharu Hirota, Shohei Yokoyama, Hiroshi Ishikawa:
Inferring Tourist Behavior and Purposes of a Twitter User. PRICAI Workshops 2016: 101-112 - 2015
- [c6]Yo Ehara, Masao Utiyama, Eiichiro Sumita:
Risk-aware distribution of SMT outputs for translation of documents targeting many anonymous readers. IWSLT 2015 - 2014
- [c5]Yo Ehara, Yusuke Miyao, Hidekazu Oiwa, Issei Sato, Hiroshi Nakagawa:
Formalizing Word Sampling for Vocabulary Prediction as Graph-based Active Learning. EMNLP 2014: 1374-1384 - 2013
- [j1]Yo Ehara, Nobuyuki Shimizu, Takashi Ninomiya, Hiroshi Nakagawa:
Personalized reading support for second-language web documents. ACM Trans. Intell. Syst. Technol. 4(2): 31:1-31:19 (2013) - [c4]Yo Ehara, Issei Sato, Hidekazu Oiwa, Hiroshi Nakagawa:
Understanding seed selection in bootstrapping. TextGraphs@EMNLP 2013: 44-52 - 2012
- [c3]Yo Ehara, Issei Sato, Hidekazu Oiwa, Hiroshi Nakagawa:
Mining Words in the Minds of Second Language Learners: Learner-Specific Word Difficulty. COLING 2012: 799-814 - 2010
- [c2]Yo Ehara, Nobuyuki Shimizu, Takashi Ninomiya, Hiroshi Nakagawa:
Personalized reading support for second-language web documents by collective intelligence. IUI 2010: 51-60
2000 – 2009
- 2008
- [c1]Yo Ehara, Kumiko Tanaka-Ishii:
Multilingual Text Entry using Automatic Language Detection. IJCNLP 2008: 441-448
Coauthor Index
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last updated on 2024-04-25 05:48 CEST by the dblp team
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