1. Articles in category: NER

    25-48 of 420 « 1 2 3 4 5 ... 16 17 18 »
    1. Artificial Intelligence Developer al+ Will Present Research Results on Fine-Grained Named Entity Recognition at the World's Most Prestigious Conference on Natural Language Processing ACL 2017

      By: Alt Inc. via Business Wire News Releases July 31, 2017 at 17:00 PM EDT Artificial Intelligence Developer al+ Will Present Research Results on Fine-Grained Named Entity Recognition at the World's Most Prestigious Conference on Natural Language Processing ACL 2017 AI Developer al+ Will Present Research Results on Fine-Grained Named Entity Recognition at the World's Most Prestigious Conference on ACL 2017 Alt Inc. ( Headquarters: Chiyoda-ku, Tokyo, CEO: Kazutaka Yonekura), the company that develops the Personal Artificial Intelligence (P.A.I.) software “al+”, will present its latest research results on Fine-Grained Named Entity Recognition at ACL 2017 (the ...

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    2. Artificial Intelligence Developer al+ Will Present Research Results on Fine-Grained Named Entity Recognition at the World's Most Prestigious Conference on Natural Language Processing ACL 2017

      Twitter News Feed Item Artificial Intelligence Developer al+ Will Present Research Results on Fine-Grained Named Entity Recognition at the World's Most Prestigious Conference on Natural Language Processing ACL 2017 July 31, 2017 05:00 PM EDT Blog This Alt Inc. ( Headquarters: Chiyoda-ku, Tokyo, CEO: Kazutaka Yonekura), the company that develops the Personal Artificial Intelligence (P.A.I.)

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    3. Artificial Intelligence Developer al+ Will Present Research Results on Fine-Grained Named Entity Recognition at the World's Most Prestigious Conference on Natural Language Processing ACL 2017

      Artificial Intelligence Developer al+ Will Present Research Results on Fine-Grained Named Entity Recognition at the World's Most Prestigious Conference on Natural Language Processing ACL 2017

      TOKYO- Alt Inc. ( Headquarters: Chiyoda-ku, Tokyo, CEO: Kazutaka Yonekura), the company that develops the Personal Artificial Intelligence (P.A.I.) software “al+”, will present its latest research results on Fine-Grained Named Entity Recognition at ACL 2017 (the Annual Meeting of the Association for Computational Linguistics), the world’s most prestigious conference on Natural Language Processing, being held from July 30th (Sunday) to August 4th (Friday) in Vancouver, Canada.

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    4. Stanford NLP Named Entity Recognition AI

      Stanford NLP Named Entity Recognition AI

      Stanford NLP Named Entity Recognition DZone's Guide to Stanford NLP Named Entity Recognition Natural language processing is a core aspect of artificial intelligence. Learn about the Stanford NLP, a tool that can be used for entity recognition. by Jul. 18, 17 · AI Zone Free Resource Join For Free This article is about setting up a Stanford NLP in a Java project using different annotators provided by the Stanford NLP for named entity recognition.

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      Mentions: Stanford POS NER
    5. An active learning-enabled annotation system for clinical named entity recognition.

      An active learning-enabled annotation system for clinical named entity recognition.

      An active learning-enabled annotation system for clinical named entity recognition.

      BMC Med Inform Decis Mak. 2017 Jul 05;17(Suppl 2):82

      Authors: Chen Y, Lask TA, Mei Q, Chen Q, Moon S, Wang J, Nguyen K, Dawodu T, Cohen T, Denny JC, Xu H

      Abstract BACKGROUND: Active learning (AL) has shown the promising potential to minimize the annotation cost while maximizing the performance in building statistical natural language processing (NLP) models.

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    6. Entity recognition from clinical texts via recurrent neural network.

      Entity recognition from clinical texts via recurrent neural network.

      Entity recognition from clinical texts via recurrent neural network.

      BMC Med Inform Decis Mak. 2017 Jul 05;17(Suppl 2):67

      Authors: Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, Xu H

      Abstract BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health information (PHI), widely exist in clinical texts.

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    7. A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations

      Contributed equally to this work with: Tome Eftimov, Barbara Koroušić Seljak, Peter Korošec * E-mail: Affiliations Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia, Jožef Stefan International Postgraduate School, Ljubljana, Slovenia ⨯ Contributed equally to this work with: Tome Eftimov, Barbara Koroušić Seljak, Peter Korošec Affiliation Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia ⨯ Peter Korošec Contributed equally to this work with: Tome Eftimov, Barbara Koroušić Seljak, Peter Korošec Affiliations Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia, Faculty of Mathematics, Natural Science and Information Technologies, Koper, Slovenia ⨯ A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations Tome Eftimov ...

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    8. DrugSemantics: a corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics.

      DrugSemantics: a corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics.

      DrugSemantics: a corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics.

      J Biomed Inform. 2017 Jun 14;:

      Authors: Moreno I, Boldrini E, Moreda P, Teresa Romá-Ferri M

      Abstract For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care.

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    9. Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks.

      Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks.

      Predicting Mental Conditions Based on "History of Present Illness" in Psychiatric Notes with Deep Neural Networks.

      J Biomed Inform. 2017 Jun 09;:

      Authors: Tran T, Kavuluru R

      Abstract BACKGROUND: Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives.

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      Mentions: NLP CNN RNN
    10. A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation

      References Alexander et al., 2015a M. Alexander, A. Baron, F. Dallachy, S. Piao, P. Rayson Metaphor, popular science and semantic tagging: Distant reading with the historical thesaurus of English Digital Scholarship Humanit., 30 (1) (2015), pp. 16–27 Alexander et al., 2015b M. Alexander, A. Baron, F. Dallachy, S. Piao, P. Rayson, S. Wattam Semantic tagging and early modern collocates Proceedings of The Corpus Linguistics 2015 Conference, Lancaster University, UK (2015), pp.

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    11. TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

      TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

      TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

      JMIR Public Health Surveill. 2017 May 03;3(2):e24

      Authors: Alvaro N, Miyao Y, Collier N

      Abstract BACKGROUND: Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner.

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      Mentions: Miyao Y NLP
    25-48 of 420 « 1 2 3 4 5 ... 16 17 18 »
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