1. Articles in category: NER

    1-24 of 381 1 2 3 4 ... 14 15 16 »
    1. 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
    2. 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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    3. 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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    4. 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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    5. 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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    6. 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
    7. 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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    8. 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
    9. Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Acronym Disambiguation in Spanish Electronic Health Narratives Using Machine Learning Techniques.

      Stud Health Technol Inform. 2017;235:251-255

      Authors: Rubio-López I, Costumero R, Ambit H, Gonzalo-Martín C, Menasalvas E, Rodríguez González A

      Abstract Electronic Health Records (EHRs) are now being massively used in hospitals what has motivated current developments of new methods to process clinical narratives (unstructured data) making it possible to perform context-based searches.

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    10. Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

      Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

      Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

      J Am Med Inform Assoc. 2017 Feb 19;:

      Authors: Zhang K, Demner-Fushman D

      Abstract Objective : To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women.

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    11. Short text messaging in digital mobile telecommunication networks

      A digital mobile telecommunications method using a digital telecommunications system. The method comprises: requesting the message from the content provider by the telecommunications device; receiving the message by the telecommunications device via the digital mobile telecommunications network from a content provider; sending the message to a text classification system by the telecommunications device via the wired digital network and the digital mobile telecommunications network; creating text tokens from the text portion using a tokenizing algorithm by the text classification system; transforming the text tokens into stemmed tokens using a stemming algorithm by the text classification system; determining a word classifier ...

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    12. Text mining for improved exposure assessment

      Affiliation Computer Laboratory, University of Cambridge, Cambridge, United Kingdom ⨯ Affiliation Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden ⨯ Affiliation Computer Laboratory, University of Cambridge, Cambridge, United Kingdom ⨯ Affiliation Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden ⨯ Affiliations Computer Laboratory, University of Cambridge, Cambridge, United Kingdom, Language Technology Lab, DTAL, University of Cambridge, Cambridge, United Kingdom ⨯ Affiliation Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden ⨯ Text mining for improved exposure assessment Kristin Larsson, Figures Abstract Chemical exposure assessments are based on information collected via different methods, such as biomonitoring, personal monitoring, environmental monitoring and questionnaires. The vast amount of chemical-specific exposure ...

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    13. What Are Chatbots and How Do They Work?

      What Are Chatbots and How Do They Work?

      February 20, 2017 • Armando Roggio Messaging chatbots may help businesses improve customer service, sell more, and earn more profit, thanks to a familiar interface and increasing customer interest. Consumers — your potential customers — use these messaging applications. In 2016, Facebook Messenger, as an example, was said to have about 900 million active users a month.

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      Mentions: NLP
    14. MetaMap Lite: an evaluation of a new Java implementation of MetaMap.

      MetaMap Lite: an evaluation of a new Java implementation of MetaMap.

      MetaMap Lite: an evaluation of a new Java implementation of MetaMap.

      J Am Med Inform Assoc. 2017 Jan 27;:

      Authors: Demner-Fushman D, Rogers WJ, Aronson AR

      Abstract MetaMap is a widely used named entity recognition tool that identifies concepts from the Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite, an implementation of some of the basic MetaMap functions in Java.

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    15. Enrichment of named entities in documents via contextual attribute ranking

      Technologies pertaining to retrieval of contextually relevant attribute values for an automatically identified named entity in a document are described herein. Named entity recognition technologies are employed to identify named entities in the text of a document. Context corresponding to an identified named entity is analyzed to probabilistically assign a class to the named entity. Attributes that are most relevant to the class are determined, and attribute values for such attributes are retrieved. The attribute values are presented in correlation with the named entity in the document responsive to user-selection of the named entity in the document.

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    16. ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition.

      ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition.

      ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition.

      Biomed Res Int. 2016;2016:4248026

      Authors: Akkasi A, Varoğlu E, Dimililer N

      Abstract Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens.

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      Mentions: NER
    17. Using OpenNLP for Named-Entity-Recognition in Scala Big Data

      Using OpenNLP for Named-Entity-Recognition in Scala Big Data

      A common challenge in Natural Language Processing (NLP) is Named Entity Recognition (NER) - this is the process of extracting specific pieces of data from a body of text, commonly people, places and organizations (for example trying to extract the name of all people mentioned in a wikipedia article). NER is a problem that has been tackled many times over the evolution of NLP, from dictionary-based, to rule-based, to statistical models and more recently using Neural Nets to solve the problem.

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      Mentions: Stanford NLP GPL
    18. Bonnie J. Dorr's Publications

      Bonnie J. Dorr's Publications

      markdown html code Bonnie J. Dorr's Publications archived 5 Nov 2016 00:42:16 UTC wiki code {{cite web | title = Bonnie J. Dorr's Publications | url = http://www.umiacs.umd.edu/~bonnie/publications.html | date = 2016-11-05 | archiveurl = http://archive.is/Pkabu | archivedate = 2016-11-05 }} Bonnie J. Dorr's Publications 2016 Dorr, Bonnie J., Craig S. Greenberg, Peter Fontana, Mark Przybocki, Marion Le Bras, Cathryn Ploehn, Oleg Aulov, Martial Michel,E.

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    19. Building a Deep Learning Powered GIF Search Engine

      Building a Deep Learning Powered GIF Search Engine

      teaching machines @openai previously: @claralabs @watchsend @mosaicio @ycombinator S13 http://tarzain.com/ yesterday Building a Deep Learning Powered GIF Search Engine How I built deepgif.tarzain.com They say a picture’s worth a thousand words, so GIFs are worth at least an order of magnitude more. But what are we to do when the experience of finding the right GIF is like searching for the right ten thousand words in a library full of books, and your only aid is the Dewey Decimal System ?

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    20. Lexalytics Simplifies and Improves Text Analytics for the Enterprise with New Machine Learning (ML) Capabilities and Feature Set

      Salience 6.2 Also Brings Emoji Analytics, Email Processing and Enhanced Named Entity Recognition to Leading Platform Boston, MA (PRWEB) October 13, 2016 Lexalytics ®, the leader in cloud and on-prem text analytics solutions, announced today that it has bolstered the machine learning (ML) capabilities of its Salience text analytics platform, making it easier for data analysts and scientists to train their Salience software to deliver actionable insights from data sources.

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      Mentions: Japan Canada Boston
    21. Lexalytics Simplifies and Improves Text Analytics for the Enterprise with New Machine Learning (ML) Capabilities and Feature Set

      By: PRWeb October 13, 2016 at 16:22 PM EDT Lexalytics Simplifies and Improves Text Analytics for the Enterprise with New Machine Learning (ML) Capabilities and Feature Set PRWeb Lexalytics (R), the leader in cloud and on-prem text analytics solutions, announced today that it has bolstered the machine learning (ML) capabilities of its Salience text analytics platform, making it easier for data analysts and scientists to train their Salience software to deliver actionable insights from data sources. In addition, Salience 6.2 now enables professionals in social media marketing, voice of the employee (VOE), voice of the customer (VOC) and ...

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      Mentions: Japan Canada Boston
    22. Lexalytics Simplifies and Improves Text Analytics for the Enterprise with New Machine Learning (ML) Capabilities and Feature Set

      Lexalytics Simplifies and Improves Text Analytics for the Enterprise with New Machine Learning (ML) Capabilities and Feature Set

      Lexalytics Simplifies and Improves Text Analytics for the Enterprise with New Machine Learning (ML) Capabilities and Feature Set Thursday, 13 October 2016 ( 34 minutes ago ) Salience 6.2 Also Brings Emoji Analytics, Email Processing and Enhanced Named Entity Recognition to Leading Platform Boston, MA (PRWEB) October 13, 2016 Lexalytics®, the leader in cloud and on-prem text analytics solutions, announced today that it has bolstered the machine learning (ML) capabilities of its Salience text analytics platform, making it easier for data analysts and scientists to train their Salience software to deliver actionable insights from data sources. In addition, Salience 6.2 ...

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      Mentions: Japan Canada Boston
    1-24 of 381 1 2 3 4 ... 14 15 16 »
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