Search results for "FrameNet"

showing 6 items of 6 documents

Chapter 11. Computational representation of FrameNet for multilingual natural language generation

2021

Computer sciencebusiness.industryRepresentation (systemics)Natural language generationArtificial intelligenceFrameNetbusinesscomputer.software_genrecomputerNatural language processing
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FrameNet-LV datu kopas transformēšana un tiešsaistes pārlūka izstrāde

2022

FrameNet-LV projekts ir balstīts uz latviešu valodas semantisku marķēšanu pēc freimu metodes. Šajā dokumentā aprakstītā programmatūra kalpo šī projekta papildināšanai ar datu transformēšanu uz XML formātu, kas ir balstīts uz Bērklija FrameNet projekta datu organizācijas struktūru, ar Python kodu pielāgojot to latviešu FrameNet vajadzībām. Dokumenta ietvaros ir aprakstīta arī šīs informācijas attēlošanas nolūkos izstrādātā tīmekļa pārlūka projektējums un realizācija. FrameNet-LV tīmekļa pārlūks ir izstrādāts ar mērķi, lai lietotājs spētu ar tā palīdzību ērtāk pārskatīt un pielietot korpusā marķēto semantisko informāciju pēc dažādiem meklēšanas un datu atlases kritērijiem.

DatorzinātneFrameNetXMLsemantiska marķēšanalatviešu valodaPython
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FrameNet CNL: A Knowledge Representation and Information Extraction Language

2014

The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL. The framework is used on natural language documents and represents the extracted knowledge in a tailor-made Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be generated automatically in multiple languages. This approach brings together the fields of information extraction and CNL, because a source text can be considered belonging to FrameNet-CNL, if information extraction parser produces the correct knowledge representation as a result. We describe a state-of-the-art information extraction parser used by a national news agency and speculate that Fram…

Information retrievalParsingKnowledge representation and reasoningbusiness.industryComputer scienceAgency (philosophy)computer.software_genreParaphraseInformation extractionArtificial intelligenceSource textFrameNetbusinesscomputerNatural language processingNatural language
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RDF* Graph Database as Interlingua for the TextWorld Challenge

2019

This paper briefly describes the top-scoring submission to the First TextWorld Problems: A Reinforcement and Language Learning Challenge. To alleviate the partial observability problem, characteristic to the TextWorld games, we split the Agent into two independent components: Observer and Actor, communicating only via the Interlingua of the RDF* graph database. The RDF* graph database serves as the “world model” memory incrementally updated by the Observer via FrameNet informed Natural Language Understanding techniques and is used by the Actor for the efficient exploration and planning of the game Action sequences. We find that the deep-learning approach works best for the Observer componen…

InterlinguaInformation retrievalGraph databaseComputer scienceBacktrackingbusiness.industryDeep learningNatural language understandingcomputer.file_formatcomputer.software_genrelanguage.human_languagelanguageReinforcement learningArtificial intelligenceRDFFrameNetbusinesscomputer2019 IEEE Conference on Games (CoG)
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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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