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    1. Polya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler

      (Save current location: Abstract Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big data sets that are best analyzed in parallel and distributed computational environments.

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      Mentions: Gibbs LDA
    2. Deep Learning on FPGAs: Past, Present, and Future

      (Save current location: Abstract The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to ground-breaking performance in important domains such as computer vision, speech recognition, and natural language processing.

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      Mentions: Fpga
    3. Deep Learning

      Deep Learning

      (Save current location: Abstract This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

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      Mentions: Monte Carlo
    4. Biomedical question answering using semantic relations.

      (Save current location: Abstract The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved.

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      Mentions: Medline
    5. Towards a medical question-answering system: a feasibility study.

      (Save current location: Abstract Question-answering (QA) systems, as have been presented and evaluated in several TREC conferences, are the next generation of search engines. They combine 'traditional' Information Retrieval (IR) with Natural Language Processing (NLP) and Knowledge Engineering techniques to provide shorter, more precise answers to natural language questions. We study here the feasibility of such a system for French in the health care domain.

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      Mentions: NLP Trec
    6. Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

      Brought to you by AQnowledge , precision products for scientists x CiteULike uses cookies, some of which may already have been set. Read about how we use cookies . We will interpret your continued use of this site as your acceptance of our use of cookies. You may hide this message. CiteULike is a free online bibliography manager. Register and you can start organising your references online.

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    7. Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods

      (Save current location: Abstract The task of paraphrasing is inherently familiar to speakers of all languages. Moreover, the task of automatically generating or extracting semantic equivalences for the various units of language?words, phrases, and sentences?is an important part of natural language processing (NLP) and is being increasingly employed to improve the performance of several NLP applications.

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      Mentions: NLP
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