Matches in ScholarlyData for { ?s ?p ?o. }
- 139 label "Ontology-Based XQuery ing of XML-Encoded Language Resources on Multiple Annotation Layers".
- 139 sameAs 139.
- 139 abstract "We present an approach for querying collections of heterogeneous linguistic corpora that are annotated on multiple layers using arbitrary XML-based markup languages. An OWL ontology provides a homogenising view on the conceptually different markup languages so that a common querying framework can be established using the method of ontology-based query expansion. In addition, we present a highly flexible web-based graphical interface that can be used to query corpora with regard to several different linguistic properties such as, for example, syntactic tree fragments. This interface can also be used for ontology-based querying of multiple corpora simultaneously.".
- 139 hasAuthorList authorList.
- 139 hasTopic Linguistics.
- 139 isPartOf proceedings.
- 139 keyword "Corpus (creation, annotation, etc.)".
- 139 keyword "LR Infrastructures and Architectures".
- 139 keyword "Ontologies".
- 139 title "Ontology-Based XQuery ing of XML-Encoded Language Resources on Multiple Annotation Layers".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
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- item-1 type ListItem.
- item-1 hasContent georg-rehm.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent richard-eckart.
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- item-3 type ListItem.
- item-3 hasContent christian-chiarcos.
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- item-4 type ListItem.
- item-4 hasContent johannes-dellert.
- item-4 previous item-3.
- 142 creator anna-korhonen.
- 142 creator cedric-messiant.
- 142 creator thierry-poibeau.
- 142 type InProceedings.
- 142 label "LexSchem: a Large Subcategorization Lexicon for French Verbs".
- 142 sameAs 142.
- 142 abstract "This paper presents LexSchem - the first large, fully automatically acquired subcategorization lexicon for French verbs. The lexicon includes subcategorization frame and frequency information for 3297 French verbs. When evaluated on a set of 20 test verbs against a gold standard dictionary, it shows 0.79 precision, 0.55 recall and 0.65 F-measure. We have made this resource freely available to the research community on the web.".
- 142 hasAuthorList authorList.
- 142 hasTopic Linguistics.
- 142 isPartOf proceedings.
- 142 keyword "Acquisition, Machine Learning".
- 142 keyword "Lexicon, lexical database".
- 142 keyword "Syntax".
- 142 title "LexSchem: a Large Subcategorization Lexicon for French Verbs".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
- authorList hasItem item-3.
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- item-1 type ListItem.
- item-1 hasContent cedric-messiant.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent thierry-poibeau.
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- item-2 previous item-1.
- item-3 type ListItem.
- item-3 hasContent anna-korhonen.
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- 143 creator aljoscha-burchardt.
- 143 creator marco-pennacchiotti.
- 143 type InProceedings.
- 143 label "FATE: a FrameNet-Annotated Corpus for Textual Entailment".
- 143 sameAs 143.
- 143 abstract "Several studies indicate that the level of predicate-argument structure is relevant for modeling prevalent phenomena in current textual entailment corpora. Although large resources like FrameNet have recently become available, attempts to integrate this type of information into a system for textual entailment did not confirm the expected gain in performance. The reasons for this are not fully obvious; candidates include FrameNet s restricted coverage, limitations of semantic parsers, or insufficient modeling of FrameNet information. To enable further insight on this issue, in this paper we present FATE (FrameNet-Annotated Textual Entailment), a manually crafted, fully reliable frame-annotated RTE corpus. The annotation has been carried out over the 800 pairs of the RTE-2 test set. This dataset offers a safe basis for RTE systems to experiment, and enables researchers to develop clearer ideas on how to effectively integrate frame knowledge in semantic inferenence tasks like recognizing textual entailment. We describe and present statistics over the adopted annotation, which introduces a new schema based on full-text annotation of so called relevant frame evoking elements.".
- 143 hasAuthorList authorList.
- 143 hasTopic Linguistics.
- 143 isPartOf proceedings.
- 143 keyword "Corpus (creation, annotation, etc.)".
- 143 keyword "Semantics".
- 143 keyword "Tools, systems, applications".
- 143 title "FATE: a FrameNet-Annotated Corpus for Textual Entailment".
- authorList type List.
- authorList hasFirstItem item-1.
- authorList hasItem item-1.
- authorList hasItem item-2.
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- item-1 type ListItem.
- item-1 hasContent aljoscha-burchardt.
- item-1 next item-2.
- item-2 type ListItem.
- item-2 hasContent marco-pennacchiotti.
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- 144 creator cedric-messiant.
- 144 creator thierry-poibeau.
- 144 type InProceedings.
- 144 label "Do we Still Need Gold Standards for Evaluation?".
- 144 sameAs 144.
- 144 abstract "The availability of a huge mass of textual data in electronic format has increased the need for fast and accurate techniques for textual data processing. Machine learning and statistical approaches have been increasingly used in NLP since a decade, mainly because they are quick, versatile and efficient. However, despite this evolution of the field, evaluation still rely (most of the time) on a comparison between the output of a probabilistic or statistical system on the one hand, and a non-statistic, most of the time hand-crafted, gold standard on the other hand. In this paper, we take the example of the acquisition of subcategorization frames from corpora as a practical example. Our study is motivated by the fact that, even if a gold standard is an invaluable resource for evaluation, a gold standard is always partial and does not really show how accurate and useful results are.".
- 144 hasAuthorList authorList.
- 144 hasTopic Linguistics.
- 144 isPartOf proceedings.
- 144 keyword "Evaluation methodologies".
- 144 keyword "Tools, systems, applications".
- 144 keyword "Validation of LRs".
- 144 title "Do we Still Need Gold Standards for Evaluation?".
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