Towards a semantic

Xem 1-20 trên 30 kết quả Towards a semantic
  • We present experiments aiming at an automatic classification of Spanish verbs into lexical semantic classes. We apply well-known techniques that have been developed for the English language to Spanish, proving that empirical methods can be re-used through languages without substantial changes in the methodology.

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  • We propose a framework for generating an abstractive summary from a semantic model of a multimodal document. We discuss the type of model required, the means by which it can be constructed, how the content of the model is rated and selected, and the method of realizing novel sentences for the summary.

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  • This paper presents a new approach to detecting and tracking changes in word meaning by visually modeling and representing diachronic development in word contexts. Previous studies have shown that computational models are capable of clustering and disambiguating senses, a more recent trend investigates whether changes in word meaning can be tracked by automatic methods. The aim of our study is to offer a new instrument for investigating the diachronic development of word senses in a way that allows for a better understanding of the nature of semantic change in general. ...

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  • We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the largescale acquisition of word-semantic information, e.g. the construction of domainindependent lexica. The backbone of the annotation are semantic roles in the frame semantics paradigm. We report experiences and evaluate the annotated data from the first project stage. On this basis, we discuss the problems of vagueness and ambiguity in semantic annotation.

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  • The purpose of this paper is to suggest that quantifiers in natural languages do not have a fixed truth functional meaning as has long been held in logical semantics. Instead we suggest that quantifiers can best be modeled as complex inference procedures that are highly dynamic and sensitive to the linguistic context, as well as time and memory constraints 1.

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  • This p a p e r discusses an approach to incremental learning in natural language processing. The t e c h n i q u e of projecting and integrating semantic c o n s t r a i n t s to learn word definitions is analyzed as Implemented in the POLITICS system. E x t e n s i o n s and improvements of this technique are developed. The problem of generalizing existing word meanings and understanding metaphorical uses of words Is addressed In terms of semantic constraint Integration.

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  • In recent years,a great deal of evidence has been collected which gives substantially increased insight into the nature of human speech perception. It is the author's belief that such data can be effectively used to infer much of the structure of a practical speech recognition system. This paper details a new view of the role of structural constraints within the several structural domains (e.g. articulation, phonetics, phonology, syntax, semantics) that must be utilized to infer the desired percept. ...

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  • In the present paper we argue that the so-called sentence adverbials (typically, adverbs like probably, admittedly,...) should be generated, in the framework of Functional Generative Description, by means of a special deep case - Complementation of Attitude (CA) on grounds of their special behaviour in the topic-focus articulation (TFA) of a sentence.

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  • The desire to construct robust and portable natural language systems has led to research o n how a core vocabulary for such systems can be defined. Stalistical methods and semantic criteria for doing this arc discussed and compared. Currcnlly it docs not seem possible to precisely define the notion of core vocabulary, but it is argued that workable criteria can nevertheless be ['o1.1110. l:inally it is emplmsized that the implementation of a core vt~cabulary must be seen as a long-range research prt~gram rather than as a short-term goal. ...

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  • The interpretation of coercion constructions (to begin a book) has been recently considered as resulting from the operation of type changing. For instance, a phrase of type o (object) is coerced to a phrase of type e (event) under the influence of the predicate. We show that this procedure encounters empirical difficulties. Focussing on the begin/commencer case, we show that the coercion interpretation results both from general semantic processes and properties of the predicate, and we argue that it is best represented at the lexical level. ...

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  • This paper reports the results of a preliminary experiment on the detection of semantic variants of terms in a French technical document. The general goal of our work is to help the structuration of terminologies. Two kinds of semantic variants can be found in traditional terminologies : strict synonymy links and fuzzier relations like see-also. We have designed three rules which exploit general dictionary information to infer synonymy relations between complex candidate terms. The results have been examined by a human terminologist.

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  • This paper introduces a machine learning method based on bayesian networks which is applied to the mapping between deep semantic representations and lexical semantic resources. A probabilistic model comprising Minimal Recursion Semantics (MRS) structures and lexicalist oriented semantic features is acquired. Lexical semantic roles enriching the MRS structures are inferred, which are useful to improve the accuracy of deep semantic parsing.

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  • As natural language understanding research advances towards deeper knowledge modeling, the tasks become more and more complex: we are interested in more nuanced word characteristics, more linguistic properties, deeper semantic and syntactic features. One such example, explored in this article, is the mention detection and recognition task in the Automatic Content Extraction project, with the goal of identifying named, nominal or pronominal references to real-world entities—mentions— and labeling them with three types of information: entity type, entity subtype and mention type. ...

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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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  • Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based systems currently make use of the FrameNet database but fail to show suitable generalization capabilities in out-of-domain scenarios. In this paper, a state-of-art system for frame-based SRL is extended through the encapsulation of a distributional model of semantic similarity. The resulting argument classification model promotes a simpler feature space that limits the potential overfitting effects....

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  • There is a widely held belief in the natural language and computational linguistics communities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering and information extraction. In this paper, we present an SRL system for Modern Standard Arabic that exploits many aspects of the rich morphological features of the language. The experiments on the pilot Arabic Propbank data show that our system based on Support Vector Machines and Kernel Methods yields a global SRL F1 score of 82.

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  • A crucial step toward the goal of automatic extraction of propositional information from natural language text is the identification of semantic relations between constituents in sentences. We examine the problem of distinguishing among seven relation types that can occur between the entities “treatment” and “disease” in bioscience text, and the problem of identifying such entities. We compare five generative graphical models and a neural network, using lexical, syntactic, and semantic features, finding that the latter help achieve high classification accuracy. ...

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  • This position paper argues for an interactive approach to text understanding. The proposed model extends an existing semantics-based text authoring system by using the input text as a source of information to assist the user in re-authoring its content. The approach permits a reliable deep semantic analysis by combining automatic information extraction with a minimal amount of human intervention.

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  • We introduce a new categorial formalism based on intuitionistic linear logic. This formalism, which derives from current type-logical grammars, is abstract in the sense that both syntax and semantics are handled by the same set of primitives. As a consequence, the formalism is reversible and provides different computational paradigms that may be freely composed together. : On the other hand, the semantic contents obeys the following scheme: : This asymmetry may be broken by: 1.

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  • We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence.

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