We manually normalize noisy Japanese expressions on social networking services (SNS) to improve the performance of sentiment polarity classification.Despite advances in pre-trained language models, informal expressions found in social media still plague natural language processing.In this study, we analyzed 6,000 posts from a sentiment analysis corpus for Japanese SNS text, and constructed a text normalization taxonomy consisting of 33 types of editing operations.Text normalization according to our taxonomy significantly improved the performance of BERT-based sentiment analysis in Japanese.Detailed analysis reveals that most types of editing operations each contribute to improve the performance of sentiment analysis.

Text Normalization for Sentiment Analysis in Japanese Social Media

We propose a method to automate orthodontic diagnosis with natural language processing. It is worthwhile to assist dentists with such technology to prevent errors by inexperienced dentists and to reduce the workload of experienced ones. However, text length and style inconsistencies in medical findings make an automated orthodontic diagnosis with deep-learning models difficult. In this study, we improve the performance of automatic diagnosis utilizing short summaries of medical findings written in a consistent style by experienced dentists. Experimental results on 970 Japanese medical findings show that summarization consistently improves the performance of various machine learning models for automated orthodontic diagnosis. Although BERT is the model that gains the most performance with the proposed method, the convolutional neural network achieved the best performance.

Automated Orthodontic Diagnosis from a Summary of Medical Findings

Emotional Intensity Estimation based on Writer's Personality

We annotate 17,000 SNS posts with both the writer’s subjective emotional intensity and the reader’s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer’s subjective labels than the readers’. The large gap between the subjective and objective emotions implies the complexity of the mapping from a post to the subjective emotional intensities, which also leads to a lower performance with machine learning models.

WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations

Commercially available light field cameras have difficulty in capturing 5D (4D + time) light field videos. They can only capture still light field images or are excessively expensive for normal users to capture the light field video. To tackle this problem, we propose a deep learning-based method for synthesizing a light field video from a monocular video. We propose a new synthetic light field video dataset that renders
photorealistic scenes using Unreal Engine because no light field video dataset is available. The proposed deep learning framework synthesizes the light field video with a full set (99) of sub-aperture images from a normal monocular video. The proposed network consists of three sub-networks, namely, feature extraction, 5D light field video synthesis, and temporal consistency refinement. Experimental results show that our model can successfully synthesize the light field video for synthetic and real scenes and outperforms the previous frame-by-frame method
quantitatively and qualitatively.

5D Light Field Synthesis from a Monocular Video

The WNUT workshop focuses on core NLP tasks (e.g., POS/NER tagging and translation) over user-generated text, such as that found on social media, web forums, online reviews, digital health records, or language learner essays. **[Read more...](https://noisy-text.github.io)**

W09: Tenth Workshop on Noisy and User-generated Text (W-NUT 2025)

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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W17: The 5th Clinical Natural Language Processing Workshop (Clinical NLP)

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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6D-Oral: Language Resources and Evaluation

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

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OS T3.3: Stereo and 3D Vision

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Hajime Nagahara

6

14

Presentations

Text Normalization for Sentiment Analysis in Japanese Social Media

Automated Orthodontic Diagnosis from a Summary of Medical Findings

Emotional Intensity Estimation based on Writer's Personality

WRIME: A New Dataset for Emotional Intensity Estimation with Subjective and Objective Annotations

5D Light Field Synthesis from a Monocular Video

5D Light Field Synthesis from a Monocular Video

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