Papers by Masayuki Asahara
Word Familiarity Rate Estimation Using a Bayesian Linear Mixed Model (D19-59)
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| Challenge: | 96,557 words were rated using the ‘Word List by Semantic Principles’ . 96 participants were surveyed using Yahoo! crowdsourcing . |
| Approach: | They used Bayesian linear mixed models to estimate word familiarity rates using the ‘Word List by Semantic Principles’ and the semantic labels used in the study. |
| Outcome: | The proposed method estimated word familiarity rates using Bayesian linear mixed models and semantic labels. |
Design of BCCWJ-EEG: Balanced Corpus with Human Electroencephalography (2020.lrec-1)
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| Challenge: | Recent research has focused on the fusion of NLP and neuroscience of language. |
| Approach: | They propose to use a balanced corpus of written Japanese (BCCWJ) annotated with human electroencephalography to improve annotations and annotations. |
| Outcome: | The proposed language resource is annotated with human electroencephalography (EEG) and can improve on annotations, genres, languages, etc. |
Universal Dependencies Version 2 for Japanese (L18-1)
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Masayuki Asahara, Hiroshi Kanayama, Takaaki Tanaka, Yusuke Miyao, Sumire Uematsu, Shinsuke Mori, Yuji Matsumoto, Mai Omura, Yugo Murawaki
| Challenge: | UD Japanese resources are built on automatic conversion from several treebanks. |
| Approach: | They propose to port the word delimitation, POS, and syntactic relations of existing treebanks to UD Japanese . they discuss the issues of the UD scheme found through porting of the Japanese language . |
| Outcome: | The proposed UD Japanese resources are based on automatic conversion from treebanks. |
Reading Time and Vocabulary Rating in the Japanese Language: Large-Scale Japanese Reading Time Data Collection Using Crowdsourcing (2022.lrec-1)
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| Challenge: | a study examines how differences in human vocabulary affect reading time . vocabulary size is inversely correlated to reading time due to the COVID-19 pandemic . |
| Approach: | They assume that vocabulary is random effect of research participants . they then asked participants to take part in a self-paced reading task to collect reading times . |
| Outcome: | The proposed method clarifies the tendency that vocabulary differences give to reading time. |
All-words Word Sense Disambiguation Using Concept Embeddings (L18-1)
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| Challenge: | Existing work on all-words word sense disambiguation (all-word WSD) uses word embeddings to identify the senses of words in documents. |
| Approach: | They propose a new concept embedding method to predict target word senses . concept embeds are constructed from concept tag sequences created from previous predictions . |
| Outcome: | The proposed concept embeddings improve Japanese all-words word sense disambiguation task. |
Dynamically Updating Event Representations for Temporal Relation Classification with Multi-category Learning (2020.findings-emnlp)
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| Challenge: | Existing models with independent classifiers for each TLINK category hinder from using the whole data. |
| Approach: | They propose a temporal relation classification model that manages dynamic event representations across multiple TLINKs using multi-task learning to leverage the full size of data. |
| Outcome: | The proposed model outperforms state-of-the-art models and two strong transfer learning baselines on English and Japanese data. |
KOTONOHA: A Corpus Concordance System for Skewer-Searching NINJAL Corpora (2020.lrec-1)
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Teruaki Oka, Yuichi Ishimoto, Yutaka Yagi, Takenori Nakamura, Masayuki Asahara, Kikuo Maekawa, Toshinobu Ogiso, Hanae Koiso, Kumiko Sakoda, Nobuko Kibe
| Challenge: | NINJAL has developed several types of corpora for linguistic research . for each corpus NINJAL provided an online search environment, ‘Chunagon’ . |
| Approach: | NINJAL has developed several types of corpora for linguistic research . for each corpus NINJAL provided an online search environment, ‘Chunagon’, which is a morphological-information-annotation-based concordance system made publicly available in 2011 . NINjal has now provided a system ‘Kotonoha’ based on the ‘Chunegon’ systems . |
| Outcome: | NINJAL has provided a skewer-search system ‘Kotonoha’ based on ‘Chunagon’ systems. |
Lower Perplexity is Not Always Human-Like (2021.acl-long)
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| Challenge: | Existing efforts to build human-like computational models have focused on English . a cross-lingual evaluation is needed to build such models, but current research has focused on Japanese . |
| Approach: | They re-examine an established generalization that lower perplexity is not always human-like in Japanese . they propose a cross-lingual evaluation to build human-type computational models . |
| Outcome: | The proposed model lacks universality and lower perplexity is not always human-like . the results suggest a cross-lingual evaluation will be necessary to build human-type models . |