1. Articles in category: WSD

    1-24 of 369 1 2 3 4 ... 14 15 16 »
    1. Distributed Representations of Words to Guide Bootstrapped Entity Classifiers

      Sonal Gupta and Christopher D. Manning. 2015. Distributed Representations of Words to Guide Bootstrapped Entity Classifiers. In In 2015 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL).

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    2. Breaking Out of Local Optima with Count Transforms and Model Recombination: A Study in Grammar Induction

      Valentin I. Spitkovsky, Hiyan Alshawi, and Daniel Jurafsky. 2013. Breaking Out of Local Optima with Count Transforms and Model Recombination: A Study in Grammar Induction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013).

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    3. A Study of Academic Collaborations in Computational Linguistics using a Latent Mixture of Authors Model

      Nikhil Johri, Daniel Ramage, Daniel A. McFarland, and Daniel Jurafsky. 2011. A Study of Academic Collaborations in Computational Linguistics using a Latent Mixture of Authors Model. In Proceedings of the ACL 2011 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities.

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    4. Techniques for understanding the aboutness of text based on semantic analysis

      In one embodiment of the present invention, a semantic analyzer translates a text segment into a structured representation that conveys the meaning of the text segment. Notably, the semantic analyzer leverages a semantic network to perform word sense disambiguation operations that map text words included in the text segment into concepts--word senses with a single, specific meaning--that are interconnected with relevance ratings. A topic generator then creates topics on-the-fly that includes one or more mapped concepts that are related within the context of the text segment. In this fashion, the topic generator tailors the semantic network to the text segment ...

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    5. Clinical Word Sense Disambiguation with Interactive Search and Classification.

      Clinical Word Sense Disambiguation with Interactive Search and Classification.

      Clinical Word Sense Disambiguation with Interactive Search and Classification.

      AMIA Annu Symp Proc. 2016;2016:2062-2071

      Authors: Wang Y, Zheng K, Xu H, Mei Q

      Abstract Resolving word ambiguity in clinical text is critical for many natural language processing applications. Effective word sense disambiguation (WSD) systems rely on training a machine learning based classifier with abundant clinical text that is accurately annotated, the creation of which can be costly and time-consuming.

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    6. The Effect of Gender in the Publication Patterns in Mathematics

      Contributed equally to this work with: Helena Mihaljević-Brandt, Lucía Santamaría, Marco Tullney * E-mail: (HMB); (LS); (MT) Affiliation Independent Researcher, Potsdam, Germany ⨯ Contributed equally to this work with: Helena Mihaljević-Brandt, Lucía Santamaría, Marco Tullney * E-mail: (HMB); (LS); (MT) Affiliation German National Library of Science and Technology (TIB), Hannover, Germany ⨯ The Effect of Gender in the Publication Patterns in Mathematics Helena Mihaljević-Brandt, Figures Abstract Despite the increasing number of women graduating in mathematics, a systemic gender imbalance persists and is signified by a pronounced gender gap in the distribution of active researchers and professors. Especially at the level of university faculty, women ...

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      Mentions: Japan India Korea
    7. Enso Associates Announces Machine Learning Framework

      Enso Associates Announces Machine Learning Framework

      Enso Associates Announces Machine Learning Framework Enso's Framework Based on State of the Art Open Source Tools • Products SANTA ROSA, Calif. - Oct. 12, 2016 - PRLog -- Enso Associates is pleased to announce the launch of their framework designed to help organizations extract, organize and use the unstructured text in their big data to make better and faster decisions.

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    8. A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).

      A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).

      A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD).

      J Am Med Inform Assoc. 2016 Aug 18;

      Authors: Wu Y, Denny JC, Rosenbloom ST, Miller RA, Giuse DA, Wang L, Blanquicett C, Soysal E, Xu J, Xu H

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    9. Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms.

      Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms.

      Corpus Domain Effects on Distributional Semantic Modeling of Medical Terms.

      Bioinformatics. 2016 Aug 16;

      Authors: Pakhomov SV, Finley G, McEwan R, Wang Y, Melton GB

      Abstract MOTIVATION: Automatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research.

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    10. Deep learning alone will never outperform natural language understanding

      Deep learning alone will never outperform natural language understanding

      Above: Language Learning Image Credit: Shutterstock Google, Microsoft, IBM, Apple, and 885 other players in the A.I. market have all been spinning their wheels in the wrong direction. Using brute force in machine learning and natural language processing ( NLP ) with advanced statistics, bots such as Siri, Echo, Viv, Hound, Skype and others fall off a cliff the moment they receive a command that is not an exact match for the engine. This is because NLP can only approximate meaning.

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    1-24 of 369 1 2 3 4 ... 14 15 16 »
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