Matches in ScholarlyData for { ?s ?p ?o. }
- 627 keyword "Lexicon, lexical database".
- 627 keyword "Semantics".
- 627 title "Using Lexical Acquisition to Enrich a Predicate Argument Reusable Database".
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- item-1 hasContent paula-cristina-vaz.
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- item-2 hasContent david-martins-de-matos.
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- item-3 hasContent nuno-j-mamede.
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- 628 creator bernardo-magnini.
- 628 creator constantin-orasan.
- 628 creator corinna-weber.
- 628 creator david-tomas.
- 628 creator elena-cabrio.
- 628 creator guenter-neumann.
- 628 creator jose-luis-vicedo.
- 628 creator laura-hasler.
- 628 creator matteo-negri.
- 628 creator milen-kouylekov.
- 628 type InProceedings.
- 628 label "The QALL-ME Benchmark: a Multilingual Resource of Annotated Spoken Requests for Question Answering".
- 628 sameAs 628.
- 628 abstract "This paper presents the QALL-ME benchmark, a multilingual resource of annotated spoken requests in the tourism domain, freely available for research purposes. The languages currently involved in the project are Italian, English, Spanish and German. It introduces a semantic annotation scheme for spoken information access requests, specifically derived from Question Answering (QA) research. In addition to pragmatic and semantic annotations, we propose three QA-based annotation levels: the Expected Answer Type, the Expected Answer Quantifier and the Question Topical Target of a request, to fully capture the content of a request and extract the sought-after information. The QALL-ME benchmark is developed under the EU-FP6 QALL-ME project which aims at the realization of a shared and distributed infrastructure for Question Answering (QA) systems on mobile devices (e.g. mobile phones). Questions are formulated by the users in free natural language input, and the system returns the actual sequence of words which constitutes the answer from a collection of information sources (e.g. documents, databases). Within this framework, the benchmark has the twofold purpose of training machine learning based applications for QA, and testing their actual performance with a rapid turnaround in controlled laboratory setting.".
- 628 hasAuthorList authorList.
- 628 hasTopic Linguistics.
- 628 isPartOf proceedings.
- 628 keyword "Corpus (creation, annotation, etc.)".
- 628 keyword "Multilinguality".
- 628 keyword "Question Answering".
- 628 title "The QALL-ME Benchmark: a Multilingual Resource of Annotated Spoken Requests for Question Answering".
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- item-1 type ListItem.
- item-1 hasContent elena-cabrio.
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- item-10 type ListItem.
- item-10 hasContent corinna-weber.
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- item-2 hasContent milen-kouylekov.
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- item-3 hasContent bernardo-magnini.
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- item-4 hasContent matteo-negri.
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- item-5 hasContent laura-hasler.
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- item-6 hasContent constantin-orasan.
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- item-6 previous item-5.
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- item-7 hasContent david-tomas.
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- item-8 hasContent jose-luis-vicedo.
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- item-9 hasContent guenter-neumann.
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- 629 creator armando-stellato.
- 629 creator maria-teresa-pazienza.
- 629 type InProceedings.
- 629 label "Clustering of Terms from Translation Dictionaries and Synonyms Lists to Automatically Build more Structured Linguistic Resources".
- 629 sameAs 629.
- 629 abstract "Building a Linguistic Resource (LR) is a task requiring a huge quantitative of means, human resources and funds. Though finalization of the development phase and assessment of the produced resource, necessarily require human involvement, a computer aided process for building the resource s initial structure would greatly reduce the overall effort to be undertaken. We present here a novel approach for automatizing the process of building structured (possibly multilingual) LRs, starting from already available LRs and exploiting simple vocabularies of synonyms and/or translations for different languages. A simple algorithm for clustering terms, according to their shared senses, is presented in two versions, both for separating flat list of synonyms and flat lists of translations. The algorithm is then motivated against two possible exploitations: reducing the cost for producing new LRs, and linguistically enriching the content of existing semantic resources, like SW ontologies and knowledge bases. Empirical results are provided for two experimental setups: automatic term clustering for English synonyms list, and for Italian translations of English terms".
- 629 hasAuthorList authorList.
- 629 hasTopic Linguistics.
- 629 isPartOf proceedings.