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HokuMed in NTCIR-11 MedNLP2:Automatic extraction of medical complaints from Japanese health records using machine learning and rule-based methods.
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In: http://arakilab.media.eng.hokudai.ac.jp/%7Earaki/2014/2014-A-6.pdf (2014)
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A Casual Conversation System Using Modality and Word Associations Retrieved from the Web
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In: http://www.aclweb.org/anthology/D08-1040/ (2008)
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A dynamic memory management system based on forgetting and recalling
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In: http://sig.media.eng.hokudai.ac.jp/~ptaszynski/data/2007_11_SHIBU_Forget_recall.pdf (2007)
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Automatic First Utterance Creation Based on the Strongest Association Retrieved from WWW
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In: http://www-kasm.nii.ac.jp/jsai2005/schedule/pdf/000245.pdf (2005)
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cs.kitami-it.ac.jp
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In: http://www.aclweb.org/anthology/W/W14/W14-2610.pdf
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Effective Analysis of Emotiveness in Utterances Based on Features of Lexical and Non-Lexical Layer of Speech.
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In: http://sig.media.eng.hokudai.ac.jp/~ptaszynski/data/2008_03_NLP_Analysis_of_Emotiveness.pdf
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Toward Automatic Support For Japanese Lay Judge System – – Processing Precedent Factors For Sentencing Trends Discovery
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In: http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings7/pdf/NTCIR7/C4/MuST-F/08-NTCIR7-MuST-F-RzepkaR.pdf
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Automatically Annotating A Five-Billion-Word Corpus of Japanese Blogs for Affect and Sentiment Analysis
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In: http://www.aclweb.org/anthology-new/W/W12/W12-3714.pdf
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Abstract:
This paper presents our research on automatic annotation of a five-billion-word corpus of Japanese blogs with information on affect and sentiment. We first perform a study in emotion blog corpora to discover that there has been no large scale emotion corpus available for the Japanese language. We choose the largest blog corpus for the language and annotate it with the use of two systems for affect analysis: ML-Ask for word- and sentence-level affect analysis and CAO for detailed analysis of emoticons. The annotated information includes affective features like sentence subjectivity (emotive/non-emotive) or emotion classes (joy, sadness, etc.), useful in affect analysis. The annotations are also generalized on a 2-dimensional model of affect to obtain information on sentence valence/polarity (positive/negative) useful in sentiment analysis. The annotations are evaluated in several ways. Firstly, on a test set of a thousand sentences extracted randomly and evaluated by over forty respondents. Secondly, the statistics of annotations are compared to other existing emotion blog corpora. Finally, the corpus is applied in several tasks, such as generation of emotion object ontology or retrieval of emotional and moral consequences of actions. 1
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.261.1806
http://www.aclweb.org/anthology-new/W/W12/W12-3714.pdf
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| 9 |
4 Connectives Acquisition in a Humanoid Robot Based on an Inductive Learning Language Acquisition Model
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In: http://cdn.intechweb.org/pdfs/6235.pdf
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15+ MILLION TOP 1% MOST CITED SCIENTIST 12.2% AUTHORS AND EDITORS FROM TOP 500 UNIVERSITIES 4 Connectives Acquisition in a Humanoid Robot Based on an Inductive Learning Language Acquisition Model
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In: http://cdn.intechopen.com/pdfs-wm/6235.pdf
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| 11 |
Commonsense Retrieval as an Aid for Easier Conversation-based Language Acquisition
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In: http://arakilab.media.eng.hokudai.ac.jp/~araki/2005(e)/2005-A-5.pdf
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| 12 |
Engagement
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In: http://arakilab.media.eng.hokudai.ac.jp/~araki/2011/2011-A-7.pdf
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Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions
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