Semantic content

Xem 1-20 trên 75 kết quả Semantic content
  • Having lived with the World Wide Web for twenty years, surfing the Web becomes a way of our life that cannot be separated. From latest news, photo sharing, social activities, to research collaborations and even commercial activities and government affairs, almost all kinds of information are available and processible via the Web. While people are appreciating the great invention, the father of the Web, Sir Tim Berners-Lee, has started the plan for the next generation of the Web, the Semantic Web....

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  • This paper presents an attempt at building a large scale distributed composite language model that simultaneously accounts for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content under a directed Markov random field paradigm.

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  • Following this tradition, many researchers recognize their usefulness in the description of language - - even if they do not agree on their significance [7]. However, a weak or strong commitment to this notion does not elude the fact that it proves to be very difficult to settle on a finite set of labels along with their formal definitions. The dilemma resulting from this challenge is well known: to require a univocal identification by each role results in an increase in their number while to abstract their semantic content gives rise to an inconsistent set. ...

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  • This paper suggests the efficient indexing method based on a concept vector space that is capable of representing the semantic content of a document. The two information measure, namely the information quantity and the information ratio, are defined to represent the degree of the semantic importance within a document. The proposed method is expected to compensate the limitations of term frequency based methods by exploiting related lexical items. Furthermore, with information ratio, this approach is independent of document length. ...

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  • This practical coursebook introduces all the basics of semantics in a simple, step-bystep fashion. Each unit includes short sections of explanation with examples, followed by stimulating practice exercises to complete the book. Feedback and comment sections follow each exercise to enable students to monitor their progress. Part 1 presents the contents as: Basic ideas in semantics, from reference, to sense, logic. Inviting you to refer.

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  • This book explains, collects and reports on the latest research results that aim at narrowing the so-called multimedia "Semantic Gap": the large disparity between descriptions of multimedia content that can be computed automatically, and the richness and subjectivity of semantics in user queries and human interpretations of audiovisual media. Addressing the grand challenge posed by the "Semantic Gap" requires a multi-disciplinary approach (computer science, computer vision and signal processing, cognitive science, web science, etc....

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  • This paper describes the use of an on-line system to do word-sense ambiguity resolution and content analysis of English paragraphs, using a system of semantic analysis programmed in Q32 LISP 1.5. The system of semantic analysis comprises dictionary codings for the text words, coded forms of permitted message, and rules producing message forms in combination on the basis of a criterion of semantic closeness. All these can be expressed as a single system of rules of phrase-structure form.

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  • It is a fundamental and important task to extract key phrases from documents. Generally, phrases in a document are not independent in delivering the content of the document. In order to capture and make better use of their relationships in key phrase extraction, we suggest exploring the Wikipedia knowledge to model a document as a semantic network, where both n-ary and binary relationships among phrases are formulated.

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  • The Automatic Content Linking Device is a just-in-time document retrieval system which monitors an ongoing conversation or a monologue and enriches it with potentially related documents, including multimedia ones, from local repositories or from the Internet. The documents are found using keyword-based search or using a semantic similarity measure between documents and the words obtained from automatic speech recognition.

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  • The integration of sophisticated inference-based techniques into natural language processing applications first requires a reliable method of encoding the predicate-argument structure of the propositional content of text. Recent statistical approaches to automated predicateargument annotation have utilized parse tree paths as predictive features, which encode the path between a verb predicate and a node in the parse tree that governs its argument.

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  • It seems rather natural to admit that language use is governed by rules that relate signs, forms and meanings to possible intentions or possible interpretations, in function of utterance situations. Not less natural should seem the idea that the meaning of a natural language expression conveys enough material to the input of these rules, so that, given the situation of utterance, they determine the appropriate interpretation.

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  • Certain restrictions on possible scopings of quantifiednoun phrases in natural language are usually expressed in terms of formal constraints on binding at a level of logical form. Such reliance on the form rather than the content of semantic interpretations goes against the spirit of compositionality. I will show that those scoping restrictions follow from simple and fundamental facts about functional application and abstraction, and can be expressed as constraints on the derivation of possible meanings for sentences rather than constraints of the alleged forms of those meanings. ...

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  • We describe an unsupervised approach to the problem of automatically detecting subgroups of people holding similar opinions in a discussion thread. An intuitive way of identifying this is to detect the attitudes of discussants towards each other or named entities or topics mentioned in the discussion. Sentiment tags play an important role in this detection, but we also note another dimension to the detection of people’s attitudes in a discussion: if two persons share the same opinion, they tend to use similar language content. ...

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  • We address a core aspect of the multilingual content synchronization task: the identification of novel, more informative or semantically equivalent pieces of information in two documents about the same topic. This can be seen as an application-oriented variant of textual entailment recognition where: i) T and H are in different languages, and ii) entailment relations between T and H have to be checked in both directions.

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  • This paper demonstrates a new method for leveraging free-text annotations to infer semantic properties of documents. Free-text annotations are becoming increasingly abundant, due to the recent dramatic growth in semistructured, user-generated online content. An example of such content is product reviews, which are often annotated by their authors with pros/cons keyphrases such as “a real bargain” or “good value.”

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  • The variation in speech due to dialect is a factor which significantly impacts speech system performance. In this study, we investigate effective methods of combining acoustic and language information to take advantage of (i) speaker based acoustic traits as well as (ii) content based word selection across the text sequence. For acoustics, a GMM based system is employed and for text based dialect classification, we proposed n-gram language models combined with Latent Semantic Analysis (LSA) based dialect classifiers. ...

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  • This paper presents an approach to detection of the semantic types of relation arguments employing the WordNet hierarchy. Using the SemEval-2007 data, we show that the method allows to generalize relation arguments with high precision for such generic relations as Origin-Entity, Content-Container, Instrument-Agency and some other.

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  • Extracting semantic relationships between entities is challenging because of a paucity of annotated data and the errors induced by entity detection modules. We employ Maximum Entropy models to combine diverse lexical, syntactic and semantic features derived from the text. Our system obtained competitive results in the Automatic Content Extraction (ACE) evaluation. Here we present our general approach and describe our ACE results.

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  • In a language generation system, a content planner embodies one or more “plans” that are usually hand–crafted, sometimes through manual analysis of target text. In this paper, we present a system that we developed to automatically learn elements of a plan and the ordering constraints among them. As training data, we use semantically annotated transcripts of domain experts performing the task our system is designed to mimic.

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  • When computer programs disambiguate words in a sentence, they often encounter non-literal or novel usages not included in their lexicon. In a recent study, Georgia Green (personal communication) estimated that 17% to 20% of the content word senses encountered in various types of normal English text are not fisted in the dictionary. While these novel word senses are generally valid, they occur in such great numbers, and with such little individual frequency that it is impractical to explicity include them all within the lexicon. ...

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