1. Articles in category: NER

    1-24 of 423 1 2 3 4 ... 16 17 18 »
    1. Entity recognition in the biomedical domain using a hybrid approach.

      Entity recognition in the biomedical domain using a hybrid approach.

      J Biomed Semantics. 2017 Nov 09;8(1):51

      Authors: Basaldella M, Furrer L, Tasso C, Rinaldi F

      Abstract BACKGROUND: This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. METHOD: The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier.

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    2. Building a natural language processing library for Apache Spark

      Building a natural language processing library for Apache Spark

      Data Show Podcast Building a natural language processing library for Apache Spark The O’Reilly Data Show Podcast: David Talby on a new NLP library for Spark, and why model development starts after a model gets deployed to production. By Ben Lorica November 9, 2017 Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher , TuneIn , iTunes , SoundCloud , RSS .

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      Mentions: Pacific NLP RSS
    3. A new synonym-substitution method to enrich the human phenotype ontology

      (Save current location: 82.166.195.66) Abstract Background: Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms. Nowadays, ontologies are one of the crucial enabling technologies in bioinformatics, providing resources for improved natural language processing tasks. However, biomedical ontology-based named entity recognition continues to be a major research problem.

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    4. NTENT Implements Applied Data Science to Identify User Intention and Increase Relevance

      NTENT Implements Applied Data Science to Identify User Intention and Increase Relevance [October 17, 2017] NTENT Implements Applied Data Science to Identify User Intention and Increase Relevance NTENT announced today a multiphase approach to understanding language that uses Applied Data Science to sift through data collected worldwide, as a means to measure user intention and provide relevant solutions.

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    5. A method for named entity normalization in biomedical articles: application to diseases and plants.

      A method for named entity normalization in biomedical articles: application to diseases and plants.

      BMC Bioinformatics. 2017 Oct 13;18(1):451

      Authors: Cho H, Choi W, Lee H

      Abstract BACKGROUND: In biomedical articles, a named entity recognition (NER) technique that identifies entity names from texts is an important element for extracting biological knowledge from articles.

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    6. NTENT Implements Applied Data Science to Identify User Intention and Increase Relevance

      NTENT Implements Applied Data Science to Identify User Intention and Increase Relevance

      NEW YORK- NTENT announced today a multiphase approach to understanding language that uses Applied Data Science to sift through data collected worldwide, as a means to measure user intention and provide relevant solutions. Applied Data Science refers to the collection and collation of information derived from a blend of data mining, data processing, predictive analytics and machine learning.

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    7. Word embedding – Data Science Group, Iitr – Medium

      Word embedding – Data Science Group, Iitr – Medium

      Oct 14 Word embedding What are word embeddings? Why we use word embeddings? Before going into details. lets see some example : There are many website that ask us to give reviews or feedback about there product when we are using them. like:- Amazon, IMDB. we also use to search at google with couple of words and get result related to it. There are some sites that put tags on the blog related the material in the bolg. so how do they do this.

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      Mentions: Amazon Machine NLP
    8. A new synonym-substitution method to enrich the human phenotype ontology.

      A new synonym-substitution method to enrich the human phenotype ontology.

      A new synonym-substitution method to enrich the human phenotype ontology.

      BMC Bioinformatics. 2017 Oct 10;18(1):446

      Authors: Taboada M, Rodriguez H, Gudivada RC, Martinez D

      Abstract BACKGROUND: Named entity recognition is critical for biomedical text mining, where it is not unusual to find entities labeled by a wide range of different terms.

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    9. 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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    10. Unsupervised Dependency Parsing without Gold Part-of-Speech Tags

      Valentin I. Spitkovsky, Hiyan Alshawi, Angel X. Chang, and Daniel Jurafsky. 2011. Unsupervised Dependency Parsing without Gold Part-of-Speech Tags. In Proceedings of the 2011 Conference on Empirical Methods on Natural Language Processing (EMNLP 2011).

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    1-24 of 423 1 2 3 4 ... 16 17 18 »
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