Evaluation of lexical resources

Xem 1-9 trên 9 kết quả Evaluation of lexical resources
  • In this paper we present a methodology for extracting subcategorisation frames based on an automatic LFG f-structure annotation algorithm for the Penn-II Treebank. We extract abstract syntactic function-based subcategorisation frames (LFG semantic forms), traditional CFG categorybased subcategorisation frames as well as mixed function/category-based frames, with or without preposition information for obliques and particle information for particle verbs.

    pdf8p bunbo_1 17-04-2013 29 1   Download

  • We present a novel approach to the automatic acquisition of a Verbnet like classification of French verbs which involves the use (i) of a neural clustering method which associates clusters with features, (ii) of several supervised and unsupervised evaluation metrics and (iii) of various existing syntactic and semantic lexical resources. We evaluate our approach on an established test set and show that it outperforms previous related work with an Fmeasure of 0.70.

    pdf10p nghetay_1 07-04-2013 33 1   Download

  • Lexical-semantic resources are used extensively for applied semantic inference, yet a clear quantitative picture of their current utility and limitations is largely missing. We propose system- and application-independent evaluation and analysis methodologies for resources’ performance, and systematically apply them to seven prominent resources. Our findings identify the currently limited recall of available resources, and indicate the potential to improve performance by examining non-standard relation types and by distilling the output of distributional methods. ...

    pdf9p bunthai_1 06-05-2013 30 1   Download

  • Recognizing entailment at the lexical level is an important and commonly-addressed component in textual inference. Yet, this task has been mostly approached by simplified heuristic methods. This paper proposes an initial probabilistic modeling framework for lexical entailment, with suitable EM-based parameter estimation. Our model considers prominent entailment factors, including differences in lexical-resources reliability and the impacts of transitivity and multiple evidence.

    pdf6p hongdo_1 12-04-2013 32 2   Download

  • Thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. Translation of such resources into multiple languages is an important component for providing multilingual access. However, the specificity of vocabulary terms in most ontologies precludes fully-automated machine translation using general-domain lexical resources. In this paper, we present an efficient process for leveraging human translations when constructing domain-specific lexical resources.

    pdf8p hongvang_1 16-04-2013 45 1   Download

  • 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.

    pdf8p bunbo_1 17-04-2013 27 1   Download

  • In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aims to provide a proper base for the inference rule application.

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  • In this paper, we present an unsupervised methodology for propagating lexical cooccurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching new concepts into the ontology. Experimental results show 73.9% attachment accuracy in the first position and 81.3% accuracy in the top-5 positions. This framework could potentially serve as a foundation for ontologizing lexical-semantic resources and assist the development of other largescale and internally consistent collections of semantic information. ...

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  • Many algorithms have been developed to harvest lexical semantic resources, however few have linked the mined knowledge into formal knowledge repositories. In this paper, we propose two algorithms for automatically ontologizing (attaching) semantic relations into WordNet. We present an empirical evaluation on the task of attaching partof and causation relations, showing an improvement on F-score over a baseline model. iati

    pdf8p hongvang_1 16-04-2013 26 1   Download



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