1. Articles in category: NER

    1-24 of 372 1 2 3 4 ... 14 15 16 »
    1. 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.

      Read Full Article
    2. 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.

      Read Full Article
    3. 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 ...

      Read Full Article
    4. 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 ...

      Read Full Article
    5. 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.

      Read Full Article
      Mentions: NLP
    6. 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.

      Read Full Article
    7. 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.

      Read Full Article
    8. 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.

      Read Full Article
      Mentions: NER
    9. 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.

      Read Full Article
      Mentions: Stanford NLP GPL
    10. 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.

      Read Full Article
    11. 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 ?

      Read Full Article
    12. 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.

      Read Full Article
      Mentions: Japan Canada Boston
    13. 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 ...

      Read Full Article
      Mentions: Japan Canada Boston
    14. 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 ...

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

      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.

      Read Full Article
      Mentions: Japan Canada Boston
    16. ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature.

      ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature.

      ChemDataExtractor: A toolkit for automated extraction of chemical information from the scientific literature.

      J Chem Inf Model. 2016 Sep 26;

      Authors: Swain MC, Cole JM

      Abstract The emergence of "big data" initiatives has led to the need for tools that can automatically extract valuable chemical information from large volumes of unstructured data, such as the scientific literature.

      Read Full Article
      Mentions: MIT
    17. Named entity resolution in spoken language processing

      Features are disclosed for determining an element of a user utterance or user intent in conjunction with one or more related elements of the user utterance or user intent. A user utterance may be transcribed by an automatic speech recognition ("ASR") module, and the results may be provided to a natural language understanding ("NLU") module. The NLU module may perform named entity recognition, intent classification, and/or other processes on the ASR results. In addition, the NLU module may determine or verify the values associated with the recognized named entities using a data store of known values. When two or ...

      Read Full Article
      Mentions: ASR NLU
    18. System and method of recording utterances using unmanaged crowds for natural language processing

      A system and method of recording utterances for building Named Entity Recognition ("NER") models, which are used to build dialog systems in which a computer listens and responds to human voice dialog. Utterances to be uttered may be provided to users through their mobile devices, which may record the user uttering (e.g., verbalizing, speaking, etc.) the utterances and upload the recording to a computer for processing. The use of the user's mobile device, which is programmed with an utterance collection application (e.g., configured as a mobile app), facilitates the use of crowd-sourcing human intelligence tasking for widespread ...

      Read Full Article
    19. Data-Driven Information Extraction from Chinese Electronic Medical Records.

      Data-Driven Information Extraction from Chinese Electronic Medical Records.

      Data-Driven Information Extraction from Chinese Electronic Medical Records.

      PLoS One. 2015;10(8):e0136270

      Authors: Xu D, Zhang M, Zhao T, Ge C, Gao W, Wei J, Zhu KQ

      Abstract OBJECTIVE: This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event.

      Read Full Article
      Mentions: SVM
    20. Predictive natural language processing models

      Features are disclosed for updating or generating natural language processing models based on information associated with items expected to be referenced in natural language processing input, such as audio of user utterances, user-entered text, etc. Natural language processing models may include, e.g., language models, acoustic models, named entity recognition models, intent classification models, and the like. The models may be updated or generated based on selected features of input data and a machine learning model trained to produce probabilities based on the selected features.

      Read Full Article
    21. Active learning for ontological event extraction incorporating named entity recognition and unknown word handling.

      Active learning for ontological event extraction incorporating named entity recognition and unknown word handling.

      Active learning for ontological event extraction incorporating named entity recognition and unknown word handling.

      J Biomed Semantics. 2016;7:22

      Authors: Han X, Kim JJ, Kwoh CK

      Abstract BACKGROUND: Biomedical text mining may target various kinds of valuable information embedded in the literature, but a critical obstacle to the extension of the mining targets is the cost of manual construction of labeled data, which are required for state-of-the-art supervised learning systems.

      Read Full Article
      Mentions: Gibbs
    22. Extraction of pharmacokinetic evidence of drug-drug interactions from the literature.

      Extraction of pharmacokinetic evidence of drug-drug interactions from the literature.

      Extraction of pharmacokinetic evidence of drug-drug interactions from the literature.

      PLoS One. 2015;10(5):e0122199

      Authors: Kolchinsky A, Lourenço A, Wu HY, Li L, Rocha LM

      Abstract Drug-drug interaction (DDI) is a major cause of morbidity and mortality and a subject of intense scientific interest. Biomedical literature mining can aid DDI research by extracting evidence for large numbers of potential interactions from published literature and clinical databases.

      Read Full Article
      Mentions: Rocha Li L MCC
    23. Techniques for updating a partial dialog state

      Embodiments provide for tracking a partial dialog state as part of managing a dialog state space, but the embodiments are not so limited. A method of an embodiment jointly models partial state update and named entity recognition using a sequence-based classification or other model, wherein recognition of named entities and a partial state update can be performed in a single processing stage at runtime to generate a distribution over partial dialog states. A system of an embodiment is configured to generate a distribution over partial dialog states at runtime in part using a sequence classification decoding or other algorithm to ...

      Read Full Article
    1-24 of 372 1 2 3 4 ... 14 15 16 »
  1. Categories

    1. Default:

      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
  2. Popular Articles

  3. Organizations in the News

    1. (1 articles) Named Entity Recognition
    2. (1 articles) Electronic Health Records
    3. (1 articles) Stud Health Technol Inform
  4. Locations in the News

    1. (1 articles) EHR