1. Articles in category: Semantic

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    1. Two Types of Hierarchies in Geospatial Ontologies

      Geospatial ontologies contain hierarchical structures, which are either based on the taxonomy of entity classes or functions and roles these entities can take. While the taxonomic hierarchies can be extracted from noun phrases contained in the formal texts that describe the geospatial domain, the hierarchies of action concepts can be traced from the verb phrases. This paper reports a simple case study of extracting the two types of such hierarchies from formal texts of traffic code. Problems of concurrent use of both hierarchies for ontology reasoning are dis-cussed, particularly, in context of the different views on geospatial ontologies. An approach ...
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    2. Word Sense Disambiguation

      Word Sense Disambiguation Content Type BookPublisher Springer NetherlandsDOI 10.1007/978-1-4020-4809-8Copyright 2006ISBN 978-1-4020-4808-1 (Print) 978-1-4020-4809-8 (Online)Editors Eneko Agirre, University of the Basque Country Department of Computer Science Manuel de Lardizabal 1 E-20018 Donostia Basque Country SpainPhilip Edmonds, Oxford Science Park Sharp Laboratories of Europe Limited OX4 4GB Oxford UK Book Series Text, Speech and Language TechnologyPrint ISSN 1386-291X Book Series Volume Volume 33
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      Mentions: Eneko Agirre
    3. Evaluation of WSD Systems

      Evaluation of WSD Systems Content Type Book ChapterDOI 10.1007/978-1-4020-4809-8_4Authors Martha Palmer, University of Colorado Departments of Linguistics and Computer Science Hellems 295 80309 Boulder CO USAHwee Ng, National University of Singapore Department of Computer Science 3 Science Drive 2 117543 SingaporeHoa Dang, National Institute of Standards and Technology 100 Bureau Drive 8940 20899-8940 Gaithersburg MD USA Book Series Text, Speech and Language TechnologyPrint ISSN 1386-291X Book Series Volume Volume 33 Book Word Sense DisambiguationDOI 10.1007/978-1-4020-4809-8Online ISBN 978-1-4020-4809-8Print ISBN 978-1-4020-4808-1
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    4. The Same Antique Web

      It’s in the air. We’re flooded by catchy phrases announcing it. It’s all about semantics, AI and Web 3.0: “Web3 is closer than you think!” “You ain’t seen nothing yet!” “Web as artificial intelligence supplanting human race!” Some years ago, “you” were the superstar of Web 2.0 and its social networks. In the late ’90s, the dot-com boom had everything going [...]
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      Mentions: Google
    5. System for normalizing a discourse representation structure and normalized data structure

      The present invention is a system and method for normalizing a discourse representation structure (DRS). The elements of the structure are rewritten and sorted in a way such that structures which may appear different but are nonetheless equivalent can be associated with the same, normalized representation. The present invention can also include a data structure for a DRS. The DRS is represented by an array of boxes, each having a set of elements which in turn has a predefined structure suitable for representing a wide variety of linguistic information.
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    6. Media agent to suggest contextually related media content

      The described arrangements and procedures provide an intelligent media agent to autonomously collect semantic multimedia data text descriptions on behalf of a user whenever and wherever the user accesses media content. The media agent analyzes these semantic multimedia data text descriptions in view of user behavior patterns and actions to assist the user in identifying multimedia content and related information that is appropriate to the context within which the user is operating or working. For instance, the media agent detects insertion of text and analyzes the inserted text. Based on the analysis, the agent predicts whether a user intends to ...
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    7. The Need for a Prescriptive Ontology

      A great deal of effort is invested in universities, research labs and companies to create prescriptive ontologies. Just think about large-scale project such as Cyc/OpenCyc or smaller projects build around OWL. I use the term “prescriptive” to emphasize the fact that ontologies are usually defined in a hard-coded and formal manner. Let’s use the “Hotel” type, [...]
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    8. Topic modeling: syntactic versus semantic

      Topic modeling has turned into a bit of a cottage industry in the NLP/machine learning world. Most seems to stem from latent Dirichlet allocation, though this of course built on previous techniques; the most well-known of which is latent semantic analysis. At the end of the day, such "topic models" really look more like dimensionality reduction techniques (eg., the similarity to multinomial PCA); however, in practice, they're often used as (perhaps soft) clustering methods. Words are mapped to t
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      Mentions: UMass
    9. Natural Language Processing and Information Systems

      Natural Language Processing and Information Systems Content Type BookPublisher Springer Berlin / HeidelbergDOI 10.1007/978-3-540-73351-5Copyright 2007ISBN 978-3-540-73350-8Editors Zoubida KedadNadira LammariElisabeth MétaisFarid MezianeYacine Rezgui Book Series Lecture Notes in Computer ScienceOnline ISSN 1611-3349Print ISSN 0302-9743 Book Series Volume Volume 4592/2007
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    10. Semantic object synchronous understanding implemented with speech application language tags

      A speech understanding system includes a language model comprising a combination of an N-gram language model and a context-free grammar language model. The language model stores information related to words and semantic information to be recognized. A module is adapted to receive input from a user and capture the input for processing. The module is further adapted to receive SALT application program interfaces pertaining to recognition of the input. The module is configured to process the SALT application program interfaces and the input to ascertain semantic information pertaining to a first portion of the input and output a semantic object ...
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    11. Semantic analysis system for interpreting linguistic structures output by a natural language linguistic analysis system

      The present invention is a system and method for performing semantic analysis that interprets a linguistic structure output by a natural language linguistic analysis system. The semantic analysis system converts the linguistic output by the natural language linguistic analysis system into a data structure model referred to as a semantic discourse representation structure (SemDRS).
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    12. Hyperbolic tree space display of computer system monitoring and analysis data

      A method for displaying a computer system runtime information includes the steps of displaying a plurality of runtime information items in different hyperbolic trees. The method further comprises the steps of navigating and inspecting runtime information within each individual hyperbolic tree, and navigating between semantically linked hyperbolic trees.
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    13. Computer system with natural language to machine language translator

      Presented is a system and method for converting or translating expressions in a natural language such as English into machine executable expressions in a formal language. This translation enables a transformation from the syntactic structures of a natural language into effective algebraic forms for further exact processing. The invention utilizes algorithms employing a reduction of sequences of terms defined over an extensible lexicon into formal syntactic and semantic structures. This term reduction incorporates both syntactic type and semantic context to achieve an effective formal representation and interpretation of the meaning conveyed by any natural language expression.
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      Mentions: Microsoft
    14. Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing

      Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing Content Type BookPublisher Springer Berlin / HeidelbergDOI 10.1007/3-540-60925-3Copyright 1996ISBN 978-3-540-60925-4Editors Stefan WermterEllen RiloffGabriele Scheler Book Series Lecture Notes in Computer ScienceOnline ISSN 1611-3349Print ISSN 0302-9743 Book Series Volume Volume 1040/1996
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