Semantics of comparative

Xem 1-20 trên 52 kết quả Semantics of comparative
  • Comparative expressions (CEs) such as "bigger than" and "more oranges than" are highly ambiguous, and their meaning is context dependent. Thus, they pose problems for the semantic interpretation algorithms typically used in natural language database interfaces. We focus on the comparison attribute ambiguities that occur with CEs. To resolve these ambiguities our natural language interface interacts with the user, finding out which of the possible interpretations was intended.

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  • Linguistic access to uncertain quantitative knowledge about physical properties is provided by d i m e n s i o n a l adjectives, e.g. long-short in the spatial and temporal senses, near-far, fast-slow, etc. Semantic analyses of the dimensional adjectives differ on whether the meaning of the differential comparative (6 cm shorter than) and the equative with factor term (three times as long as) is a compositional function of the meanings the difference and factor terms (6 cm and three times) and the meanings of the simple comparative and equative, respectively.

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  • Acquiring information systems specifications from natural language description is presented as a problem class that requires a different treatment of semantics when compared with other applied NL systems such as database and operating system interfaces. Within this problem class, the specific task of obtaining explicit conceptual data models from natural language text or dialogue is being investigated. The knowledge brought to bear on this task is classified into syntactic, semantic and systems analysis knowledge.

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  • In this paper we study unsupervised word sense disambiguation (WSD) based on sense definition. We learn low-dimensional latent semantic vectors of concept definitions to construct a more robust sense similarity measure wmfvec. Experiments on four all-words WSD data sets show significant improvement over the baseline WSD systems and LDA based similarity measures, achieving results comparable to state of the art WSD systems.

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  • This paper analyses the syntax and semantics of English comparatives, and some types of ellipsis. It improves on other recent analyses in the computational linguistics literature in three respects: (i) it uses no tree- or logical-form rewriting devices in building meaning representations (ii) this results in a fully reversible linguistic description, equally suited for analysis or generation (iii) the analysis extends to types of elliptical comparative not elsewhere treated.

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  • A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank. These corpora define the semantic roles of predicates for each frame independently. Thus, it is crucial for the machine-learning approach to generalize semantic roles across different frames, and to increase the size of training instances.

    pdf9p hongphan_1 14-04-2013 37 4   Download

  • This paper addresses the automatic classification of semantic relations in noun phrases based on cross-linguistic evidence from a set of five Romance languages. A set of novel semantic and contextual English– Romance NP features is derived based on empirical observations on the distribution of the syntax and meaning of noun phrases on two corpora of different genre (Europarl and CLUVI). The features were employed in a Support Vector Machines algorithm which achieved an accuracy of 77.9% (Europarl) and 74.

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  • We present two approaches for syntactic and semantic transfer based on LFG f-structures and compare the results with existing co-description and restriction operator based approaches, focusing on aspects of ambiguity preserving transfer, complex cases of syntactic structural mismatches as well as on modularity and reusability. The two transfer approaches are interfaced with an existing, implemented transfer component (Verbmobi1), by translating f-structures into a term language, and by interfacing fstructure representations with an existing semantic based transfer approach, respectively. ...

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  • Our research aims at building computational models of word meaning that are perceptually grounded. Using computer vision techniques, we build visual and multimodal distributional models and compare them to standard textual models. Our results show that, while visual models with state-of-the-art computer vision techniques perform worse than textual models in general tasks (accounting for semantic relatedness), they are as good or better models of the meaning of words with visual correlates such as color terms, even in a nontrivial task that involves nonliteral uses of such words. ...

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  • Tree SRL system‖ is a Semantic Role Labelling supervised system based on a tree-distance algorithm and a simple k-NN implementation. The novelty of the system lies in comparing the sentences as tree structures with multiple relations instead of extracting vectors of features for each relation and classifying them. The system was tested with the English CoNLL-2009 shared task data set where 79% accuracy was obtained.

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  • Comparative News Summarization aims to highlight the commonalities and differences between two comparable news topics. In this study, we propose a novel approach to generating comparative news summaries. We formulate the task as an optimization problem of selecting proper sentences to maximize the comparativeness within the summary and the representativeness to both news topics. We consider semantic-related cross-topic concept pairs as comparative evidences, and consider topic-related concepts as representative evidences....

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  • Semantic role labels are the representation of the grammatically relevant aspects of a sentence meaning. Capturing the nature and the number of semantic roles in a sentence is therefore fundamental to correctly describing the interface between grammar and meaning. In this paper, we compare two annotation schemes, PropBank and VerbNet, in a task-independent, general way, analysing how well they fare in capturing the linguistic generalisations that are known to hold for semantic role labels, and consequently how well they grammaticalise aspects of meaning.

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  • Iterative bootstrapping algorithms are typically compared using a single set of handpicked seeds. However, we demonstrate that performance varies greatly depending on these seeds, and favourable seeds for one algorithm can perform very poorly with others, making comparisons unreliable. We exploit this wide variation with bagging, sampling from automatically extracted seeds to reduce semantic drift. However, semantic drift still occurs in later iterations.

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  • Monolingual translation probabilities have recently been introduced in retrieval models to solve the lexical gap problem. They can be obtained by training statistical translation models on parallel monolingual corpora, such as question-answer pairs, where answers act as the “source” language and questions as the “target” language. In this paper, we propose to use as a parallel training dataset the definitions and glosses provided for the same term by different lexical semantic resources.

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  • There have been many proposals to extract semantically related words using measures of distributional similarity, but these typically are not able to distinguish between synonyms and other types of semantically related words such as antonyms, (co)hyponyms and hypernyms. We present a method based on automatic word alignment of parallel corpora consisting of documents translated into multiple languages and compare our method with a monolingual syntax-based method.

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  • We propose a method for extracting semantic orientations of words: desirable or undesirable. Regarding semantic orientations as spins of electrons, we use the mean field approximation to compute the approximate probability function of the system instead of the intractable actual probability function. We also propose a criterion for parameter selection on the basis of magnetization. Given only a small number of seed words, the proposed method extracts semantic orientations with high accuracy in the experiments on English lexicon. The result is comparable to the best value ever reported. ...

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  • In this paper, we present a method for the semantic tagging of word chunks extracted from a written transcription of conversations. This work is part of an ongoing project for an information extraction system in the field of maritime Search And Rescue (SAR). Our purpose is to automatically annotate parts of texts with concepts from a SAR ontology. Our approach combines two knowledge sources a SAR ontology and the Wordsmyth dictionarythesaurus, and it uses a similarity measure for the classification.

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  • To segment texts in thematic units, we present here how a basic principle relying on word distribution can be applied on different kind of texts. We start from an existing method well adapted for scientific texts, and we propose its adaptation to other kinds of texts by using semantic links between words. These relations are found in a lexical network, automatically built from a large corpus. We will compare their results and give criteria to choose the more suitable method according to text characteristics. ...

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  • Our aim is to use conventional MRDs, with no explicit semantic coding, to obtain a comparable accuracy. The system we propose is capable of 1) performing fully automatic extraction (with a counterpart in terms of both recall and precision fall) of taxonomic links of dictionary senses and 2) ranking the extracted relations in a w a y that selective manual refinement is allowed.

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  • Hypernym links acquired through an information extraction procedure are projected on multi-word terms through the recognition of semantic variations. The quality of the projected links resulting from corpus-based acquisition is compared with projected links extracted from a technical thesaurus. 1 Motivation In the domain of corpus-based terminology, there are two m a i n topics of research: term acquisition--the discovery of candidate terms-and automatic thesaurus construction--the addition of semantic links to a term bank. ...

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