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2010 IEEE Fourth International Conference on Semantic Computing

22-24 Sept. 2010

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Displaying Results 1 - 25 of 105
  • [Front cover]

    Publication Year: 2010, Page(s): C1
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  • [Title page i]

    Publication Year: 2010, Page(s): i
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  • [Title page iii]

    Publication Year: 2010, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2010, Page(s): iv
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  • Table of contents

    Publication Year: 2010, Page(s):v - xi
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  • Message from General Co-chairs and Conference Co-chairs

    Publication Year: 2010, Page(s): xii
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  • Message from Program Co-chairs

    Publication Year: 2010, Page(s): xiii
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  • Message from SWARCH-DL 2010 Chairs

    Publication Year: 2010, Page(s): xiv
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  • Organizing Committee

    Publication Year: 2010, Page(s):xv - xvi
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  • Program Committee

    Publication Year: 2010, Page(s):xvii - xviii
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  • Keynotes

    Publication Year: 2010, Page(s):xix - xxii
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (122 KB)

    Provides an abstract for each of the keynote presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Keynotes on Semantic Challenges

    Publication Year: 2010, Page(s): xxiii
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (110 KB)

    Provides an abstract for each of the invited presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Extracting Events and Temporal Expressions from Text

    Publication Year: 2010, Page(s):1 - 8
    Cited by:  Papers (2)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (504 KB) | HTML iconHTML

    Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal structure of documents and apply the temporal structure in other applications like textual entailment, question answering, dialog systems or others. In... View full abstract»

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  • The Semantic Vectors Package: New Algorithms and Public Tools for Distributional Semantics

    Publication Year: 2010, Page(s):9 - 15
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (210 KB) | HTML iconHTML

    Distributional semantics is the branch of natural language processing that attempts to model the meanings of words, phrases and documents from the distribution and usage of words in a corpus of text. In the past three years, research in this area has been accelerated by the availability of the Semantic Vectors package, a stable, fast, scalable, and free software package for creating and exploring ... View full abstract»

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  • Striking a Balance: Human and Computer Contributions to Learning through Semantic Analysis

    Publication Year: 2010, Page(s):16 - 23
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (508 KB) | HTML iconHTML

    Manual acquisition of high-quality, broad-coverage knowledge needed by knowledge-based NLP systems is commonly considered too expensive a procedure, and has been known to cause "the knowledge acquisition bottleneck". The use of the web as a corpus to support automating knowledge acquisition has been gaining in popularity in the recent years. This approach tends to introduce noise at the early stag... View full abstract»

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  • Simple Unsupervised Identification of Low-Level Constituents

    Publication Year: 2010, Page(s):24 - 31
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (772 KB) | HTML iconHTML

    We present an approach to unsupervised partial parsing: the identification of low-level constituents (which we dub clumps) in unannotated text. We begin by showing that CCLParser (Seginer 2007), an unsupervised parsing model, is particularly adept at identifying clumps, and that, surprisingly, building a simple right-branching structure above its clumps actually outperforms the full parser itself,... View full abstract»

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  • Developing a Dataset for Technology Structure Mining

    Publication Year: 2010, Page(s):32 - 39
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (443 KB) | HTML iconHTML

    This paper describes steps that have been taken to construct a development dataset for the task of Technology Structure Mining. We have defined the proposed task as the process of mapping a scientific corpus into a labeled digraph named a Technology Structure Graph as described in the paper. The generated graph expresses the domain semantics in terms of interdependencies between pairs of technolog... View full abstract»

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  • Leveraging Gene Ontology Annotations to Improve a Memory-Based Language Understanding System

    Publication Year: 2010, Page(s):40 - 45
    Cited by:  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (396 KB) | HTML iconHTML

    This work evaluates how detailed knowledge about proteins can be leveraged for language understanding and disambiguation by OpenDMAP. OpenDMAP is a memory-based language understanding system that uses patterns to identify concepts in text. These patterns match not only lexical elements, such as words, but also semantic elements, such as references to proteins. This work started with an existing pa... View full abstract»

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  • Ontology and Model Alignment as a Means for Requirements Validation

    Publication Year: 2010, Page(s):46 - 51
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (176 KB) | HTML iconHTML

    This paper reports on work that is investigating the application of ontology engineering and natural language processing to software engineering. Our focus is the transition from requirements to design which remains one of the main challenges in software engineering. A key reason for why this is so challenging is that the vast majority of requirements documents are informal, written in natural lan... View full abstract»

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  • A Robust Semantic Parser Designed for Spoken Dialog Sytems

    Publication Year: 2010, Page(s):52 - 55
    Cited by:  Patents (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (742 KB) | HTML iconHTML

    This article describes a knowledge oriented semantic parser designed for spoken dialog systems. It merges some syntactic and semantic aspects into a single frame to get robust and efficient analyses. The knowledge is entirely described withing an XML formalism. The parser takes as input either a string of words (the 1-best word sequence) or a word-lattice coming from an ASR (Automatic Speech Recog... View full abstract»

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  • Reference Resolution Supporting Lexical Disambiguation

    Publication Year: 2010, Page(s):56 - 59
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (357 KB) | HTML iconHTML

    This paper describes ongoing work in carrying out the semantic analysis of texts and reference resolution in a control structure that permits each process to inform the other, rather than in a more traditional, unidirectional fashion (semantics followed by reference resolution). We concentrate on situations in which a polysemous predicate cannot be lexically disambiguated until the meaning of one ... View full abstract»

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  • Joint-AL: Joint Discriminative and Generative Active Learning for Cross-Domain Semantic Concept Classification

    Publication Year: 2010, Page(s):60 - 66
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (639 KB) | HTML iconHTML

    As multimedia data come from a wide variety of domains, each having its distinctive data distributions, cross-domain video semantic concept classification becomes an important task in semantic computing. Its challenge arises from the different distribution (in feature space) of the concept between the source and the target domain, which makes a classifier trained on a source domain perform poorly ... View full abstract»

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  • Comparative Study on Subject Classification of Academic Videos Using Noisy Transcripts

    Publication Year: 2010, Page(s):67 - 72
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1203 KB) | HTML iconHTML

    With the advance of Web technologies, the number of "academic" videos available on the Web (e.g., online lectures, web seminars, conference presentations, or tutorial videos) has increased explosively. A fundamental task of managing such videos is to classify them into relevant subjects. For this task, most of current content providers rely on keywords to perform the classification, while active t... View full abstract»

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  • Automated Illustration of News Stories

    Publication Year: 2010, Page(s):73 - 78
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1301 KB) | HTML iconHTML

    Forgetting what one has just read is, in some cases, linked to insufficient attention. The reader might feel either bored or distracted by something more interesting - a common trace in children and the elderly. The challenge is: how can multimedia systems assist readers in reading and remembering stories? Several studies [2, 7, 14] showed that reading memory is improved by visual stimulus. In thi... View full abstract»

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  • A Joint Content-Event Model for Event-Centric Multimedia Indexing

    Publication Year: 2010, Page(s):79 - 84
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2536 KB) | HTML iconHTML

    In this paper, a joint content-event model for indexing multimedia data is proposed. The event part of the model follows a number of formal principles to represent several aspects of real-life events, whereas the content part is used to describe the decomposition of any type of multimedia data to content segments. In contrast to other event models for multimedia indexing, the proposed model treats... View full abstract»

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