0000000000178649

AUTHOR

Peteris Paikens

showing 3 related works from this author

Pini Language and PiniTree Ontology Editor: Annotation and Verbalisation for Atomised Journalism

2020

We present a new ontology language Pini and the PiniTree ontology editor supporting it. Despite Pini language bearing lot of similarities with RDF, UML class diagrams, Property Graphs and their frontends like Google Knowledge Graph and Protege, it is a more expressive language enabling FrameNet-style natural language annotation for Atomised journalism use case.

Computer science05 social sciences050801 communication & media studies02 engineering and technologycomputer.file_formatOntology languageProtégéLinguisticsAnnotation0508 media and communicationsUnified Modeling Language0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingJournalismClass diagramRDFcomputerNatural languagecomputer.programming_language
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FrameNet Resource Grammar Library for GF

2012

In this paper we present an ongoing research investigating the possibility and potential of integrating frame semantics, particularly FrameNet, in the Grammatical Framework (GF) application grammar development. An important component of GF is its Resource Grammar Library (RGL) that encapsulates the low-level linguistic knowledge about morphology and syntax of currently more than 20 languages facilitating rapid development of multilingual applications. In the ideal case, porting a GF application grammar to a new language would only require introducing the domain lexicon - translation equivalents that are interlinked via common abstract terms. While it is possible for a highly restricted CNL,…

Morphology (linguistics)GrammarComputer sciencebusiness.industrymedia_common.quotation_subjectGrammatical Frameworkcomputer.software_genreLexiconSyntaxConstructed languageNounFrame semanticsArtificial intelligenceArgument (linguistics)FrameNetbusinesscomputerNatural language processingmedia_common
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Riga: from FrameNet to Semantic Frames with C6.0 Rules

2015

For the purposes of SemEval-2015 Task-18 on the semantic dependency parsing we combined the best-performing closed track approach from the SemEval-2014 competition with state-of-the-art techniques for FrameNet semantic parsing. In the closed track our system ranked third for the semantic graph accuracy and first for exact labeled match of complete semantic graphs. These results can be attributed to the high accuracy of the C6.0 rule-based sense labeler adapted from the FrameNet parser. To handle large SemEval training data the C6.0 algorithm was extended to provide multi-class classification and to use fast greedy search without significant accuracy loss compared to exhaustive search. A met…

ParsingComputer sciencebusiness.industryArtificial intelligenceFrameNetcomputer.software_genrebusinesscomputerNatural language processingSemEvalGraphProceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
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