0000000000178642

AUTHOR

Arturs Znotins

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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Domain Expert Platform for Goal-Oriented Dialog Collection

2021

Today, most dialogue systems are fully or partly built using neural network architectures. A crucial prerequisite for the creation of a goal-oriented neural network dialogue system is a dataset that represents typical dialogue scenarios and includes various semantic annotations, e.g. intents, slots and dialogue actions, that are necessary for training a particular neural network architecture. In this demonstration paper, we present an easy to use interface and its back-end which is oriented to domain experts for the collection of goal-oriented dialogue samples. The platform not only allows to collect or write sample dialogues in a structured way, but also provides a means for simple annotat…

Subject-matter expertService (systems architecture)AnnotationGoal orientationHuman–computer interactionComputer scienceInterface (Java)Sample (statistics)Dialog boxDomain (software engineering)Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
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Multilingual Clustering of Streaming News

2018

Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual story clusters. Unlike typical clustering approaches that consider a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. Our method is simple to implement, computationally efficient and produces state-of-the-art …

FOS: Computer and information sciencesComputer Science - Computation and LanguageInformation retrievalComputer scienceInformationSystems_INFORMATIONSTORAGEANDRETRIEVAL02 engineering and technologyClusteringMedia MonitoringComputer Science - Information RetrievalComputingMethodologies_PATTERNRECOGNITIONMultilingual Methods0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingCluster analysisComputation and Language (cs.CL)Information Retrieval (cs.IR)
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