1. Articles in category: Semantic

    49-72 of 4040 « 1 2 3 4 5 6 ... 167 168 169 »
    1. Semantically aware, dynamic, multi-modal concordance for unstructured information analysis

      An apparatus includes a data processing system for generating and displaying a semantic type concordance. The data processing system includes memory storing a computer program, a display to display data of a concordance generated by the program, and a processor configured to execute the computer program. The computer program includes instructions for displaying a user interface configured to enable a user to select semantic types and specify at least one text document, generating a concordance of the at least one document based on the semantic types, and displaying data of the generated concordance on the display.

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    2. Detecting semantic errors in text using ontology-based extraction rules

      Semantic errors in a natural language text document are automatically detected by matching sentences in the document with stored ontology-based extraction rules that express both logically correct and logically incorrect relationships between the classes and properties of an ontology for a predefined knowledge domain of relevance to the natural language text document. The matching identifies logically correct and incorrect statements in the document which may be used for various applications such as automatic grading.

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    3. Systems and methods for creating, navigating, and searching informational web neighborhoods

      Systems and methods for the creation of hierarchical networks of overlapping informational web neighborhoods using percolation crawling. Each neighborhood comprises a set of closely linked pages that share a common set of concepts and intent and purpose. The neighborhoods represent web pages that share a common set of underlying concepts and semantic associations. Each such neighborhood can be semantically tagged.

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    4. Written word refinement system and method

      A method for processing an original user writing, implemented by a computer processor, to modify relationships between words, phrases, signs and symbols comprising the writing, where necessary, to generate a modified writing that more clearly conveys a semantic content intended by the user, or consistent with the core principles associated with its mechanisms, when compared to the original user writing. The method includes receiving an original writing from a user, processing to perform a linguistic analysis on the original user writing in accordance with a plurality of rules to identify semantic content and based on the processing, and the semantic ...

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    5. Measuring accuracy of semantic graphs with exogenous datasets

      Provided is a process including: obtaining a semantic similarity graph having nodes corresponding to documents in an analyzed corpus and edges indicating semantic similarity between pairs of the documents; for at least a plurality of nodes in the graph, evaluating accuracy of the edges based on neighboring nodes and an external corpus by performing operations including: identifying the neighboring nodes based on adjacency to the respective node in the graph; selecting documents from an external corpus based on references in the selected documents to entities mentioned in the documents of the neighboring nodes; and determining how semantically similar the respective ...

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    6. Method for extracting semantic distance from mathematical sentences and classifying mathematical sentences by semantic distance, device therefor, and computer readable recording medium

      A method of extracting the semantic distance from the mathematical sentence and classifying the mathematical sentence by the semantic distance, includes: receiving a user query; extracting at least one keyword included in the received user query; and extracting a semantic distance by, indexing one or more of natural language tokens and mathematical equation tokens including semantic information, extracting the semantic distance, between the at least one extracted keyword and the one or more indexed semantic information by referring indexed information, and acquiring a similarity of the received user query and the semantic information.

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    7. 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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    8. 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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    9. 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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    10. 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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    11. 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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    12. 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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    13. 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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    14. 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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    15. 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
    16. 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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    17. 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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    18. 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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    19. 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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    20. 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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    21. 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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    22. 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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    49-72 of 4040 « 1 2 3 4 5 6 ... 167 168 169 »
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