Eiji Aramaki


2021

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End-to-end Biomedical Entity Linking with Span-based Dictionary Matching
Shogo Ujiie | Hayate Iso | Shuntaro Yada | Shoko Wakamiya | Eiji Aramaki
Proceedings of the 20th Workshop on Biomedical Language Processing

Disease name recognition and normalization is a fundamental process in biomedical text mining. Recently, neural joint learning of both tasks has been proposed to utilize the mutual benefits. While this approach achieves high performance, disease concepts that do not appear in the training dataset cannot be accurately predicted. This study introduces a novel end-to-end approach that combines span representations with dictionary-matching features to address this problem. Our model handles unseen concepts by referring to a dictionary while maintaining the performance of neural network-based models. Experiments using two major datasaets demonstrate that our model achieved competitive results with strong baselines, especially for unseen concepts during training.

2020

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Offensive Language Detection on Video Live Streaming Chat
Zhiwei Gao | Shuntaro Yada | Shoko Wakamiya | Eiji Aramaki
Proceedings of the 28th International Conference on Computational Linguistics

This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time. Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions. We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful). To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning). We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity. Our results show that the transfer of features from social media to live streaming is effective. However, the two measurements show less correlation in the transferability prediction.

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Towards a Versatile Medical-Annotation Guideline Feasible Without Heavy Medical Knowledge: Starting From Critical Lung Diseases
Shuntaro Yada | Ayami Joh | Ribeka Tanaka | Fei Cheng | Eiji Aramaki | Sadao Kurohashi
Proceedings of the 12th Language Resources and Evaluation Conference

Applying natural language processing (NLP) to medical and clinical texts can bring important social benefits by mining valuable information from unstructured text. A popular application for that purpose is named entity recognition (NER), but the annotation policies of existing clinical corpora have not been standardized across clinical texts of different types. This paper presents an annotation guideline aimed at covering medical documents of various types such as radiography interpretation reports and medical records. Furthermore, the annotation was designed to avoid burdensome requirements related to medical knowledge, thereby enabling corpus development without medical specialists. To achieve these design features, we specifically focus on critical lung diseases to stabilize linguistic patterns in corpora. After annotating around 1100 electronic medical records following the annotation scheme, we demonstrated its feasibility using an NER task. Results suggest that our guideline is applicable to large-scale clinical NLP projects.

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Classification of Nostalgic Music Through LDA Topic Modeling and Sentiment Analysis of YouTube Comments in Japanese Songs
Kongmeng Liew | Yukiko Uchida | Nao Maeura | Eiji Aramaki
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)

2019

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Learning to Select, Track, and Generate for Data-to-Text
Hayate Iso | Yui Uehara | Tatsuya Ishigaki | Hiroshi Noji | Eiji Aramaki | Ichiro Kobayashi | Yusuke Miyao | Naoaki Okazaki | Hiroya Takamura
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We propose a data-to-text generation model with two modules, one for tracking and the other for text generation. Our tracking module selects and keeps track of salient information and memorizes which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. Our proposed model is considered to simulate the human-like writing process that gradually selects the information by determining the intermediate variables while writing the summary. In addition, we also explore the effectiveness of the writer information for generations. Experimental results show that our proposed model outperforms existing models in all evaluation metrics even without writer information. Incorporating writer information further improves the performance, contributing to content planning and surface realization.

2018

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J-MeDic: A Japanese Disease Name Dictionary based on Real Clinical Usage
Kaoru Ito | Hiroyuki Nagai | Taro Okahisa | Shoko Wakamiya | Tomohide Iwao | Eiji Aramaki
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2017

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Multivariate Linear Regression of Symptoms-related Tweets for Infectious Gastroenteritis Scale Estimation
Ryo Takeuchi | Hayate Iso | Kaoru Ito | Shoko Wakamiya | Eiji Aramaki
Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)

To date, various Twitter-based event detection systems have been proposed. Most of their targets, however, share common characteristics. They are seasonal or global events such as earthquakes and flu pandemics. In contrast, this study targets unseasonal and local disease events. Our system investigates the frequencies of disease-related words such as “nausea”,“chill”,and “diarrhea” and estimates the number of patients using regression of these word frequencies. Experiments conducted using Japanese 47 areas from January 2017 to April 2017 revealed that the detection of small and unseasonal event is extremely difficult (overall performance: 0.13). However, we found that the event scale and the detection performance show high correlation in the specified cases (in the phase of patient increasing or decreasing). The results also suggest that when 150 and more patients appear in a high population area, we can expect that our social sensors detect this outbreak. Based on these results, we can infer that social sensors can reliably detect unseasonal and local disease events under certain conditions, just as they can for seasonal or global events.

2016

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MedNLPDoc: Japanese Shared Task for Clinical NLP
Eiji Aramaki | Yoshinobu Kano | Tomoko Ohkuma | Mizuki Morita
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

Due to the recent replacements of physical documents with electronic medical records (EMR), the importance of information processing in medical fields has been increased. We have been organizing the MedNLP task series in NTCIR-10 and 11. These workshops were the first shared tasks which attempt to evaluate technologies that retrieve important information from medical reports written in Japanese. In this report, we describe the NTCIR-12 MedNLPDoc task which is designed for more advanced and practical use for the medical fields. This task is considered as a multi-labeling task to a patient record. This report presents results of the shared task, discusses and illustrates remained issues in the medical natural language processing field.

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Detecting Japanese Patients with Alzheimer’s Disease based on Word Category Frequencies
Daisaku Shibata | Shoko Wakamiya | Ayae Kinoshita | Eiji Aramaki
Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP)

In recent years, detecting Alzheimer disease (AD) in early stages based on natural language processing (NLP) has drawn much attention. To date, vocabulary size, grammatical complexity, and fluency have been studied using NLP metrics. However, the content analysis of AD narratives is still unreachable for NLP. This study investigates features of the words that AD patients use in their spoken language. After recruiting 18 examinees of 53–90 years old (mean: 76.89), they were divided into two groups based on MMSE scores. The AD group comprised 9 examinees with scores of 21 or lower. The healthy control group comprised 9 examinees with a score of 22 or higher. Linguistic Inquiry and Word Count (LIWC) classified words were used to categorize the words that the examinees used. The word frequency was found from observation. Significant differences were confirmed for the usage of impersonal pronouns in the AD group. This result demonstrated the basic feasibility of the proposed NLP-based detection approach.

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Forecasting Word Model: Twitter-based Influenza Surveillance and Prediction
Hayate Iso | Shoko Wakamiya | Eiji Aramaki
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Because of the increasing popularity of social media, much information has been shared on the internet, enabling social media users to understand various real world events. Particularly, social media-based infectious disease surveillance has attracted increasing attention. In this work, we specifically examine influenza: a common topic of communication on social media. The fundamental theory of this work is that several words, such as symptom words (fever, headache, etc.), appear in advance of flu epidemic occurrence. Consequently, past word occurrence can contribute to estimation of the number of current patients. To employ such forecasting words, one can first estimate the optimal time lag for each word based on their cross correlation. Then one can build a linear model consisting of word frequencies at different time points for nowcasting and for forecasting influenza epidemics. Experimentally obtained results (using 7.7 million tweets of August 2012 – January 2016), the proposed model achieved the best nowcasting performance to date (correlation ratio 0.93) and practically sufficient forecasting performance (correlation ratio 0.91 in 1-week future prediction, and correlation ratio 0.77 in 3-weeks future prediction). This report is the first of the relevant literature to describe a model enabling prediction of future epidemics using Twitter.

2015

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Location Name Disambiguation Exploiting Spatial Proximity and Temporal Consistency
Takashi Awamura | Daisuke Kawahara | Eiji Aramaki | Tomohide Shibata | Sadao Kurohashi
Proceedings of the third International Workshop on Natural Language Processing for Social Media

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Who caught a cold ? - Identifying the subject of a symptom
Shin Kanouchi | Mamoru Komachi | Naoaki Okazaki | Eiji Aramaki | Hiroshi Ishikawa
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Disease Event Detection based on Deep Modality Analysis
Yoshiaki Kitagawa | Mamoru Komachi | Eiji Aramaki | Naoaki Okazaki | Hiroshi Ishikawa
Proceedings of the ACL-IJCNLP 2015 Student Research Workshop

2013

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Word in a Dictionary is used by Numerous Users
Eiji Aramaki | Sachiko Maskawa | Mai Miyabe | Mizuki Morita | Sachi Yasuda
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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The First Workshop on Natural Language Processing for Medical and Healthcare Fields
Eiji Aramaki | Mizuki Morita
The First Workshop on Natural Language Processing for Medical and Healthcare Fields

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Incorporating Knowledge Resources to Enhance Medical Information Extraction
Yasuhide Miura | Tomoko Ohkuma | Hiroshi Masuichi | Emiko Yamada Shinohara | Eiji Aramaki | Kazuhiko Ohe
The First Workshop on Natural Language Processing for Medical and Healthcare Fields

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Clinical Vocabulary and Clinical Finding Concepts in Medical Literature
Takashi Okumura | Eiji Aramaki | Yuka Tateisi
The First Workshop on Natural Language Processing for Medical and Healthcare Fields

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Proper and Efficient Treatment of Anaphora and Long-Distance Dependency in Context-Free Grammar: An Experiment with Medical Text
Wailok Tam | Koiti Hasida | Yusuke Matsubara | Eiji Aramaki | Mai Miyabe | Motoyuki Takaai | Hirosi Uozaki
The First Workshop on Natural Language Processing for Medical and Healthcare Fields

2011

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Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter
Eiji Aramaki | Sachiko Maskawa | Mizuki Morita
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Using Various Features in Machine Learning to Obtain High Levels of Performance for Recognition of Japanese Notational Variants
Masahiro Kojima | Masaki Murata | Jun’ichi Kazama | Kow Kuroda | Atsushi Fujita | Eiji Aramaki | Masaaki Tsuchida | Yasuhiko Watanabe | Kentaro Torisawa
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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Adverse-Effect Relations Extraction from Massive Clinical Records
Yasuhide Miura | Eiji Aramaki | Tomoko Ohkuma | Masatsugu Tonoike | Daigo Sugihara | Hiroshi Masuichi | Kazuhiko Ohe
Proceedings of the Second Workshop on NLP Challenges in the Information Explosion Era (NLPIX 2010)

2009

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TEXT2TABLE: Medical Text Summarization System Based on Named Entity Recognition and Modality Identification
Eiji Aramaki | Yasuhide Miura | Masatsugu Tonoike | Tomoko Ohkuma | Hiroshi Mashuichi | Kazuhiko Ohe
Proceedings of the BioNLP 2009 Workshop

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Fast Decoding and Easy Implementation: Transliteration as Sequential Labeling
Eiji Aramaki | Takeshi Abekawa
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

2008

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Orthographic Disambiguation Incorporating Transliterated Probability
Eiji Aramaki | Takeshi Imai | Kengo Miyo | Kazuhiko Ohe
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2007

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Support vector machine based orthographic disambiguation
Eiji Aramaki | Takeshi Imai | Kengo Miyo | Kazuhiko Ohe
Proceedings of the 11th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages: Papers

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UTH: SVM-based Semantic Relation Classification using Physical Sizes
Eiji Aramaki | Takeshi Imai | Kengo Miyo | Kazuhiko Ohe
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

2005

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Probabilistic Model for Example-based Machine Translation
Eiji Aramaki | Sadao Kurohashi | Hideki Kashioka | Naoto Kato
Proceedings of Machine Translation Summit X: Papers

Example-based machine translation (EBMT) systems, so far, rely on heuristic measures in retrieving translation examples. Such a heuristic measure costs time to adjust, and might make its algorithm unclear. This paper presents a probabilistic model for EBMT. Under the proposed model, the system searches the translation example combination which has the highest probability. The proposed model clearly formalizes EBMT process. In addition, the model can naturally incorporate the context similarity of translation examples. The experimental results demonstrate that the proposed model has a slightly better translation quality than state-of-the-art EBMT systems.

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Toward Medical Ontology using Natural Language Processing
Eiji Aramaki | Takeshi Imai | Masayo Kashiwagi | Masayuki Kajino | Kengo Miyo | Kazuhiko Ohe
Proceedings of OntoLex 2005 - Ontologies and Lexical Resources

2004

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Example-based machine translation using structural translation examples
Eiji Aramaki | Sadao Kurohashi
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

2003

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Word Selection for EBMT based on Monolingual Similarity and Translation Confidence
Eiji Aramaki | Sadao Kurohashi | Hideki Kashioka | Hideki Tanaka
Proceedings of the HLT-NAACL 2003 Workshop on Building and Using Parallel Texts: Data Driven Machine Translation and Beyond

2001

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Finding translation correspondences from parallel parsed corpus for example-based translation
Eiji Aramaki | Sadao Kurohashi | Satoshi Sato | Hideo Watanabe
Proceedings of Machine Translation Summit VIII

This paper describes a system for finding phrasal translation correspondences from parallel parsed corpus that are collections paired English and Japanese sentences. First, the system finds phrasal correspondences by Japanese-English translation dictionary consultation. Then, the system finds correspondences in remaining phrases by using sentences dependency structures and the balance of all correspondences. The method is based on an assumption that in parallel corpus most fragments in a source sentence have corresponding fragments in a target sentence.

2000

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Finding Structural Correspondences from Bilingual Parsed Corpus for Corpus-based Translation
Hideo Watanabe | Sadao Kurohashi | Eiji Aramaki
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics