1. Articles in category: Semantic

    49-72 of 4027 « 1 2 3 4 5 6 ... 166 167 168 »
    1. 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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    2. 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
    3. 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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    4. 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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    5. 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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    6. 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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    7. 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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    8. 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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    9. 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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    10. 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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    11. 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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    12. 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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    13. 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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    14. IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner. (arXiv:1605.01194v1 [cs.CL])

      Interpretable semantic textual similarity (iSTS) task adds a crucial explanatory layer to pairwise sentence similarity. We address various components of this task: chunk level semantic alignment along with assignment of similarity type and score for aligned chunks with a novel system presented in this paper. We propose an algorithm, iMATCH, for the alignment of multiple non-contiguous chunks based on Integer Linear Programming (ILP).

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    15. Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction. (arXiv:1604.06721v1 [cs.AI])

      We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language.

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    16. A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes.

      A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes.

      A high throughput semantic concept frequency based approach for patient identification: a case study using type 2 diabetes mellitus clinical notes.

      AMIA Annu Symp Proc. 2010;2010:857-61

      Authors: Wei WQ, Tao C, Jiang G, Chute CG

      Abstract UNLABELLED: Current research on high throughput identification of patients with a specific phenotype is in its infancy. There is an urgent need to develop a general automatic approach for patient identification.

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    17. Predicting Lexical Relations between Biomedical Terms: towards a Multilingual Morphosemantics-based System.

      Predicting Lexical Relations between Biomedical Terms: towards a Multilingual Morphosemantics-based System.

      Predicting Lexical Relations between Biomedical Terms: towards a Multilingual Morphosemantics-based System.

      Stud Health Technol Inform. 2005;116:793-8

      Authors: Namer F, Baud R

      Abstract This paper addresses the issue of how semantic information can be automatically assigned to compound terms, i.e. both a definition and a set of semantic relations. This issue is particularly crucial when elaborating multilingual databases and when developing cross-language information retrieval systems.

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    18. A Process for the Representation of openEHR ADL Archetypes in OWL Ontologies.

      A Process for the Representation of openEHR ADL Archetypes in OWL Ontologies.

      A Process for the Representation of openEHR ADL Archetypes in OWL Ontologies.

      Stud Health Technol Inform. 2015;216:827-31

      Authors: Porn AM, Peres LM, Didonet Del Fabro M

      Abstract ADL is a formal language to express archetypes, independent of standards or domain. However, its specification is not precise enough in relation to the specialization and semantic of archetypes, presenting difficulties in implementation and a few available tools.

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    19. Semantic search by means of word sense disambiguation using a lexicon

      Techniques are disclosed for analyzing a "context window" of a search query to determine a semantic meaning of a search word and to filter search results based upon the semantic meaning. Generally, a lexicon may be used to store forms, meanings, and usages of words and phrases. When a user specifies a query, a semantic analyzer obtains all of the word senses for a search word. The semantic analyzer applies lexical analysis techniques to the search word and context window to obtain a total score for each word sense and selects the word sense with the highest total score. After ...

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    49-72 of 4027 « 1 2 3 4 5 6 ... 166 167 168 »
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    1. (1 articles) AMIA Annu Symp Proc