Search Results for author: Antonio Pareja-Lora

Found 6 papers, 0 papers with code

LITHME: Language in the Human-Machine Era

no code implementations EAMT 2022 Maarit Koponen, Kais Allkivi-Metsoja, Antonio Pareja-Lora, Dave Sayers, Márta Seresi

The LITHME COST Action brings together researchers from various fields of study focusing on language and technology.

Preliminary Results on the Evaluation of Computational Tools for the Analysis of Quechua and Aymara

no code implementations LREC 2022 Marcelo Yuji Himoro, Antonio Pareja-Lora

This research has focused on evaluating the existing open-source morphological analyzers for two of the most widely spoken indigenous macrolanguages in South America, namely Quechua and Aymara.

Towards a Spell Checker for Zamboanga Chavacano Orthography

no code implementations LREC 2020 Marcelo Yuji Himoro, Antonio Pareja-Lora

Following its introduction as a subject and a medium of instruction in the public schools of Zamboanga City from Grade 1 to 3 in 2012, an official orthography for this variety - the so-called {``}Zamboanga Chavacano Orthography{''} - has been approved in 2014.

Machine Translation Translation

The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud

no code implementations LREC 2016 John Philip McCrae, Christian Chiarcos, Francis Bond, Philipp Cimiano, Thierry Declerck, Gerard de Melo, Jorge Gracia, Sebastian Hellmann, Bettina Klimek, Steven Moran, Petya Osenova, Antonio Pareja-Lora, Jonathan Pool

The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections.

Standardisation and Interoperation of Morphosyntactic and Syntactic Annotation Tools for Spanish and their Annotations

no code implementations LREC 2014 Antonio Pareja-Lora, Guillermo C{\'a}rcamo-Escorza, Alicia Ballesteros-Calvo

This low level of interoperability makes it difficult to reuse both the linguistic annotation tools and their annotations in new scenarios, e. g., in natural language processing (NLP) pipelines.

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