1. Articles in category: Semantic

    73-96 of 4058 « 1 2 3 4 5 6 7 ... 167 168 169 »
    1. 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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    2. Methods, apparatuses, systems and computer readable mediums to create documents and templates using domain ontology concepts

      A document and template creation system includes a document and template creation device. The document and template creation device is configured to identify at least one domain ontology concept based on at least a portion of a text-string input into a document, propose the at least one domain ontology concept for selection by the user, and insert at least one of the at least one domain ontology concept into the document in response to selection by the user.

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    3. Recommending mobile device activities

      Techniques for recommending mobile device activities, such as accessing mobile applications and/or mobile Web pages, are described. Some embodiments provide an Activity Recommendation System ("ARS") configured to recommend relevant activities for a user to perform with a mobile device, based on context of the mobile device. In one embodiment, the ARS recommends mobile applications based content items (e.g., Web pages, images, videos) that are being currently accessed via the mobile device. The ARS may process information about mobile applications and content items to determine semantic information, such as entities and/or categories referenced or associated therewith. The ARS ...

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    4. System and method for enhancing search relevancy using semantic keys

      A method, computer-usable medium, and a computer system for searching for webpages are disclosed. Embodiments of the present invention provide a convenient and efficient mechanism for filtering results from a keyword search using semantic keys and semantic sub-keys, thereby enabling an increased number of irrelevant results to be filtered from a keyword search. The search query may be parsed to determine the focus of the query, where the focus may be used determine at least one semantic key for the search query. Each semantic key may be associated with at least one semantic sub-key, where the semantic keys and/or ...

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    5. A Vector Space for Distributional Semantics for Entailment. (arXiv:1607.03780v1 [cs.CL])

      Distributional semantics creates vector-space representations that capture many forms of semantic similarity, but their relation to semantic entailment has been less clear. We propose a vector-space model which provides a formal foundation for a distributional semantics of entailment. Using a mean-field approximation, we develop approximate inference procedures and entailment operators over vectors of probabilities of features being known (versus unknown).

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    6. AudioSentibank: Large-scale Semantic Ontology of Acoustic Concepts for Audio Content Analysis. (arXiv:1607.03766v1 [cs.SD])

      Audio carries substantial information about the content of our surroundings. The content has been explored at the semantic level using acoustic concepts, but rarely on concept pairs such as happy crowd and angry crowd. Concept pairs convey unique information and complement other audio and multimedia applications. Hence, in this work we explored for the first time the classification's performance of acoustic concepts pairs.

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    7. Retrieving and viewing medical images

      As medical imaging becomes more affordable, and the diversity of diagnostic modalities and therapeutic treatments increase, the amount of data being stored increases, and the problem becomes even more critical. One approach to improve retrieval efficiency of images is to employ semantics to establish a defined set of search and classification terms. However, such semantic systems still require the user to make a selection of the most appropriate term or terms to classify a report or image, and the accuracy of the results are thus dependent on the skill and knowledge of the classifier. According to a first aspect of ...

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    8. A Distributional Semantics Approach to Implicit Language Learning. (arXiv:1606.09058v1 [cs.CL])

      In the present paper we show that distributional information is particularly important when considering concept availability under implicit language learning conditions. Based on results from different behavioural experiments we argue that the implicit learnability of semantic regularities depends on the degree to which the relevant concept is reflected in language use.

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    9. Knowledge Author: facilitating user-driven, domain content development to support clinical information extraction.

      Knowledge Author: facilitating user-driven, domain content development to support clinical information extraction.

      Knowledge Author: facilitating user-driven, domain content development to support clinical information extraction.

      J Biomed Semantics. 2016;7(1):42

      Authors: Scuba W, Tharp M, Mowery D, Tseytlin E, Liu Y, Drews FA, Chapman WW

      Abstract BACKGROUND: Clinical Natural Language Processing (NLP) systems require a semantic schema comprised of domain-specific concepts, their lexical variants, and associated modifiers to accurately extract information from clinical texts.

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      Mentions: NLP Liu Y
    10. Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings. (arXiv:1606.06368v1 [cs.LG])

      Can we train a system that, on any new input, either says "don't know" or makes a prediction that is guaranteed to be correct? We answer the question in the affirmative provided our model family is well-specified. Specifically, we introduce the unanimity principle: only predict when all models consistent with the training data predict the same output. We operationalize this principle for semantic parsing, the task of mapping utterances to logical forms.

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    11. A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language. (arXiv:1606.06274v1 [cs.CL])

      Semantic roles play an important role in extracting knowledge from text. Current unsupervised approaches utilize features from grammar structures, to induce semantic roles. The dependence on these grammars, however, makes it difficult to adapt to noisy and new languages. In this paper we develop a data-driven approach to identifying semantic roles, the approach is entirely unsupervised up to the point where rules need to be learned to identify the position the semantic role occurs.

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    12. Product Classification in E-Commerce using Distributional Semantics. (arXiv:1606.06083v1 [cs.AI])

      Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title. For efficient product classification we require a suitable representation for a document (the textual description of a product) feature vector and efficient and fast algorithms for prediction. To address the above challenges, we propose a new distributional semantics representation for document vector formation.

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    13. Two Discourse Driven Language Models for Semantics. (arXiv:1606.05679v1 [cs.CL])

      Natural language understanding often requires deep semantic knowledge. Expanding on previous proposals, we suggest that some important aspects of semantic knowledge can be modeled as a language model if done at an appropriate level of abstraction. We develop two distinct models that capture semantic frame chains and discourse information while abstracting over the specific mentions of predicates and entities.

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    14. Data Recombination for Neural Semantic Parsing. (arXiv:1606.03622v1 [cs.CL])

      Modeling crisp logical regularities is crucial in semantic parsing, making it difficult for neural models with no task-specific prior knowledge to achieve good results. In this paper, we introduce data recombination, a novel framework for injecting such prior knowledge into a model. From the training data, we induce a high-precision synchronous context-free grammar, which captures important conditional independence properties commonly found in semantic parsing.

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    15. Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change. (arXiv:1606.02821v1 [cs.CL])

      Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of semantic change.

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    16. Learning Semantically and Additively Compositional Distributional Representations. (arXiv:1606.02461v1 [cs.CL])

      This paper connects a vector-based composition model to a formal semantics, the Dependency-based Compositional Semantics (DCS). We show theoretical evidence that the vector compositions in our model conform to the logic of DCS. Experimentally, we show that vector-based composition brings a strong ability to calculate similar phrases as similar vectors, achieving near state-of-the-art on a wide range of phrase similarity tasks and relation classification; meanwhile, DCS can guide building vectors for structured queries that can be directly executed. We evaluate this utility on sentence completion task and report a new state-of-the-art.

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    17. Neural Semantic Role Labeling with Dependency Path Embeddings. (arXiv:1605.07515v1 [cs.CL])

      This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques. Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, are not handled well by existing models. Our model treats such instances as sub-sequences of lexicalized dependency paths and learns suitable embedding representations.

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    18. New cybersecurity technique uses semantic gaps to detect website promotional attacks

      New cybersecurity technique uses semantic gaps to detect website promotional attacks

      Science | | Researchers have developed a new cybersecurity technique, known as Semantic Inconsistency Search (SEISE), that uses natural language processing to spot the differences between a compromised site’s expected content and malicious advertising and promotional code, often a sign of attacks that install malware or drive traffic to websites hosting illegal commerce. Full story at

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    19. Acquisition of semantic class lexicons for query tagging

      A user's search experience may be enhanced by providing additional content based upon an understanding of the user's intent. Query tagging, the assigning of semantic labels to terms within a query, is one technique that may be utilized to determine the context of a user's search query. Accordingly, as provided herein, a query tagging model may be updated using one or more stratified lexicons. A list data structure (e.g., lists of phrases obtained from web pages) and seed distribution data (e.g., pre-labeled probability data) may be used by a graph learning technique to obtain an ...

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    20. Ontology-based annotations and semantic relations in large-scale (epi)genomics data.

      Ontology-based annotations and semantic relations in large-scale (epi)genomics data.

      Ontology-based annotations and semantic relations in large-scale (epi)genomics data.

      Brief Bioinform. 2016 May 3;

      Authors: Galeota E, Pelizzola M

      Abstract Public repositories of large-scale biological data currently contain hundreds of thousands of experiments, including high-throughput sequencing and microarray data. The potential of using these resources to assemble data sets combining samples previously not associated is vastly unexplored.

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    73-96 of 4058 « 1 2 3 4 5 6 7 ... 167 168 169 »
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