1. Articles in category: Semantic

    1-24 of 4040 1 2 3 4 ... 167 168 169 »
    1. On-page SEO in 2017: Optimizing for RankBrain & Semantic Search -

      2.3K #growthhacks #localseo #seo We've been hearing about "semantic search" since Google's Hummingbird update back in 2013. Now, Google's leveraging it in numerous ways: the Knowledge Graph, RankBrain, LSI, natural language processing algorithms... In today's post on 5 major aspects of semantic search (and their implications for SEO), I'm doing my best to explain how semantic search works and what you need (and don't need!) to change in your strategy to grow rankings in 2017.

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    2. Exhaustive automatic processing of textual information

      A system for natural language processing is provided. A first natural language processing program may be constructed using language-independent semantic descriptions, and language-dependent morphological descriptions, lexical descriptions, and syntactic descriptions of one or more target languages. The natural language processing program may include any of machine translation, fact extraction, semantic indexing, semantic search, sentiment analysis, document classification, summarization, big data analysis, or another program. Additional sets of natural language processing programs may be constructed.

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    3. Systems and methods for coreference resolution using selective feature activation

      In some aspects, systems, methods, and computer-readable media for selective feature activation for coreference resolution are disclosed. In one embodiment, a method includes receiving text data comprising a plurality of mentions corresponding to entities, and determining a plurality of data features, comprising semantic features and syntactic features, for comparing a particular pair of mentions from the plurality of mentions. The method also includes selectively activating a subset of features from the plurality of data features based on semantic and syntactic context of the particular pair of mentions within the text data, and determining, using weights associated with the activated subset ...

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    4. Techniques for understanding the aboutness of text based on semantic analysis

      In one embodiment of the present invention, a semantic analyzer translates a text segment into a structured representation that conveys the meaning of the text segment. Notably, the semantic analyzer leverages a semantic network to perform word sense disambiguation operations that map text words included in the text segment into concepts--word senses with a single, specific meaning--that are interconnected with relevance ratings. A topic generator then creates topics on-the-fly that includes one or more mapped concepts that are related within the context of the text segment. In this fashion, the topic generator tailors the semantic network to the text segment ...

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    5. Converting a text sentence to a series of images

      A text sentence is automatically converted to an image sentence that conveys semantic roles of the text sentence. This is accomplished by identifying semantic roles associated with each verb of a sentence, any associated verb adjunctions, and identifying the grammatical dependencies between words and phrases in a sentence, in some embodiments. An image database, in which each image is tagged with descriptive information corresponding to the image depicted, is queried for images corresponding to the semantic roles of the identified verbs. Unless a single image is found to depict every semantic role, the text sentence is split into two smaller ...

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    6. Automated Transformation of openEHR Data Instances to OWL.

      Automated Transformation of openEHR Data Instances to OWL.

      Automated Transformation of openEHR Data Instances to OWL.

      Stud Health Technol Inform. 2016;223:63-70

      Authors: Haarbrandt B, Jack T, Marschollek M

      Abstract Standard-based integration and semantic enrichment of clinical data originating from electronic medical records has shown to be critical to enable secondary use.

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    7. Creating ontologies by analyzing natural language texts

      Systems and methods for creating ontologies by analyzing natural language texts. An example method comprises: receiving a plurality of semantic structures associated with a text corpus; identifying a first semantic structure and a second semantic structure, wherein the first semantic structure comprises a first substructure and a second substructure, wherein the second semantic structure comprises a third substructure and a fourth substructure, and wherein the first substructure is similar to the third substructure in view of a first similarity criterion; and responsive to determining that the second substructure is similar to the fourth substructure in view of a second similarity ...

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    8. Analytic solution with a self-learning and context-sensitive semantic layer

      According to a general aspect, a system includes a query engine configured to receive a query from a user via a user interface layer for obtaining data from one or more databases, determine if a keyword of the query can be mapped to at least one of a plurality of keyword mappings stored in a semantic layer, and if the keyword cannot be mapped, provide an interactive object, via the user interface layer, to learn a new keyword mapping for the keyword such that the semantics layer is updated with the new keyword mapping for future queries. The system includes ...

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    9. Ontology-based Semantic Support to Improve Accessibility of Graphics.

      Ontology-based Semantic Support to Improve Accessibility of Graphics.

      Ontology-based Semantic Support to Improve Accessibility of Graphics.

      Stud Health Technol Inform. 2015;217:255-60

      Authors: Murillo-Morales T, Miesenberger K

      Abstract We aim to ease the process of authoring accessible graphics as well as taking a first step towards the long-term goal of allowing blind persons to access graphics autonomously.

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    10. Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.

      Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.

      Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.

      J Biomed Inform. 2017 Apr 09;:

      Authors: He B, Dong B, Guan Y, Yang J, Jiang Z, Yu Q, Cheng J, Qu C

      Abstract OBJECTIVE: To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain. MATERIALS AND METHODS: An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation ...

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      Mentions: NLP IAA
    11. Natural language processing (sentiment classification) API for French text

      Natural language processing (sentiment classification) API for French text

      Good point about the influence of the lexicon on classifier accuracy. You're right, the topic of the corpus I have is esoteric, so a conventional lexicon would perform poorly. – Escher Apr 12 '15 at 21:21 up vote 1 down vote Semantics is a difficult topic in NLP... Stanford has some great NLP tools that I suggest you take a look at, though they don't have anything for semantic analysis as far as I can tell. If this is for a large, ongoing project I implore you to write your own classifier!

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      Mentions: Stanford Bayes NLP
    12. Search and search optimization using a pattern of a location identifier

      Systems and methods for search and search optimization using a pattern in a location identifier is disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of search and search optimization. The method includes, detecting a set of location identifiers that have a pattern that matches a specified pattern and identifying a set of search results as having content related to the semantic type. The specified pattern can be stored in a computer-readable storage medium and corresponds to a semantic type. The set of search results can include objects associated with ...

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    13. Hybrid machine-user learning system and process for identifying, accurately selecting and storing scientific data

      A process for identifying, accurately selecting, and storing scientific data that is present in textual formats. The process includes providing scientific data located in a text document and searching the text document using a computer and selecting a plurality of key words and phrases using an algorithm. The selected key words and phrases are matched with a plurality of semantic definitions and a plurality of semantic definition-key words and phrase pairs are created. The created plurality of semantic definition-key words and phrase pairs are displayed to a user via a computer user interface and the user selects which of the ...

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    14. Cross-language text classification

      Methods are described for performing classification (categorization) of text documents written in various languages. Language-independent semantic structures are constructed before classifying documents. These structures reflect lexical, morphological, syntactic, and semantic properties of documents. The methods suggested are able to perform cross-language text classification which is based on document properties reflecting their meaning. The methods are applicable to genre classification, topic detection, news analysis, authorship analysis, etc.

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    15. System and method for generating and using user ontological models for natural language processing of user-provided text

      A method, system, and computer program product for generating and using a user ontological model for natural language processing of user-provided text, including receiving definitions of user ontological objects and generating user ontological models. A semantic-syntactic tree generated from user-provided text is analyzed. Information objects based on the user ontological objects are generated by the analysis.

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    16. Automatic extraction of named entities from texts

      Disclosed are systems, computer-readable mediums, and methods for extracting named entities from an untagged corpus of texts. Generating a set of attributes for each of the tokens based at least on a deep semantic-syntactic analysis. The set of attributes include lexical, syntactic, and semantic attributes. Selecting a subset of the attributes for each of the tokens. Retrieving classifier attributes and categories based on a trained model, wherein the classifier attributes are related to one or more categories. Comparing the subset of the attributes for each of the tokens with the classifier attributes. Classifying each of tokens to at least one ...

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    17. Integration of semantic context information

      In one implementation, a computer-implemented method includes receiving, at a computer system, a request to predict a next word in a dialog being uttered by a speaker; accessing, by the computer system, a neural network comprising i) an input layer, ii) one or more hidden layers, and iii) an output layer; identifying the local context for the dialog of the speaker; selecting, by the computer system and using a semantic model, at least one vector that represents the semantic context for the dialog; applying input to the input layer of the neural network, the input comprising i) the local context ...

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    18. Ontology-Oriented Programming for Biomedical Informatics.

      Ontology-Oriented Programming for Biomedical Informatics.

      Ontology-Oriented Programming for Biomedical Informatics.

      Stud Health Technol Inform. 2016;221:64-8

      Authors: Lamy JB

      Abstract Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites.

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    19. Context-sensitive search using a deep learning model

      A search engine is described herein for providing search results based on a context in which a query has been submitted, as expressed by context information. The search engine operates by ranking a plurality of documents based on a consideration of the query, and based, in part, on a context concept vector and a plurality of document concept vectors, both generated using a deep learning model (such as a deep neural network). The context concept vector is formed by a projection of the context information into a semantic space using the deep learning model. Each document concept vector is formed ...

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    20. Computing semantic similarity between biomedical concepts using new information content approach.

      Computing semantic similarity between biomedical concepts using new information content approach.

      Computing semantic similarity between biomedical concepts using new information content approach.

      J Biomed Inform. 2016 Feb;59:258-75

      Authors: Ben Aouicha M, Hadj Taieb MA

      Abstract The exploitation of heterogeneous clinical sources and healthcare records is fundamental in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that enables the processing and structuring of textual resources.

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    1-24 of 4040 1 2 3 4 ... 167 168 169 »
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