no code implementations • SemEval (NAACL) 2022 • Eduards Mukans, Gus Strazds, Guntis Barzdins
Described are our two entries “emukans” and “guntis” for the definition modeling track of CODWOE SemEval-2022 Task 1.
no code implementations • LREC 2022 • Baiba Saulite, Roberts Darģis, Normunds Gruzitis, Ilze Auzina, Kristīne Levāne-Petrova, Lauma Pretkalniņa, Laura Rituma, Peteris Paikens, Arturs Znotins, Laine Strankale, Kristīne Pokratniece, Ilmārs Poikāns, Guntis Barzdins, Inguna Skadiņa, Anda Baklāne, Valdis Saulespurēns, Jānis Ziediņš
LNCC is a diverse collection of Latvian language corpora representing both written and spoken language and is useful for both linguistic research and language modelling.
Cultural Vocal Bursts Intensity Prediction Language Modelling
1 code implementation • 11 Sep 2023 • Karlis Freivalds, Emils Ozolins, Guntis Barzdins
Integer factorization is a famous computational problem unknown whether being solvable in the polynomial time.
no code implementations • 17 Jul 2019 • Guntis Barzdins, Eduards Sidorovics
It has long been speculated that deep neural networks function by discovering a hierarchical set of domain-specific core concepts or patterns, which are further combined to recognize even more elaborate concepts for the classification or other machine learning tasks.
2 code implementations • EMNLP 2018 • Sebastião Miranda, Artūrs Znotiņš, Shay B. Cohen, Guntis Barzdins
Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories.
no code implementations • WS 2018 • Ulrich Germann, Ren{\=a}rs Liepins, Didzis Gosko, Guntis Barzdins
The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes.
no code implementations • ACL 2018 • Ulrich Germann, Ren{\=a}rs Liepins, Guntis Barzdins, Didzis Gosko, Mir, Sebasti{\~a}o a, David Nogueira
The open-source SUMMA Platform is a highly scalable distributed architecture for monitoring a large number of media broadcasts in parallel, with a lag behind actual broadcast time of at most a few minutes.
no code implementations • SEMEVAL 2017 • Normunds Gruzitis, Didzis Gosko, Guntis Barzdins
By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017 Task 9 established AMR as a powerful semantic interlingua.
no code implementations • EACL 2017 • Renars Liepins, Ulrich Germann, Guntis Barzdins, Alex Birch, ra, Steve Renals, Susanne Weber, Peggy van der Kreeft, Herv{\'e} Bourlard, Jo{\~a}o Prieto, Ond{\v{r}}ej Klejch, Peter Bell, Alex Lazaridis, ros, Alfonso Mendes, Sebastian Riedel, Mariana S. C. Almeida, Pedro Balage, Shay B. Cohen, Tomasz Dwojak, Philip N. Garner, Andreas Giefer, Marcin Junczys-Dowmunt, Hina Imran, David Nogueira, Ahmed Ali, Mir, Sebasti{\~a}o a, Andrei Popescu-Belis, Lesly Miculicich Werlen, Nikos Papasarantopoulos, Abiola Obamuyide, Clive Jones, Fahim Dalvi, Andreas Vlachos, Yang Wang, Sibo Tong, Rico Sennrich, Nikolaos Pappas, Shashi Narayan, Marco Damonte, Nadir Durrani, Sameer Khurana, Ahmed Abdelali, Hassan Sajjad, Stephan Vogel, David Sheppey, Chris Hernon, Jeff Mitchell
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +5
no code implementations • 20 Jun 2016 • Normunds Gruzitis, Guntis Barzdins
In the era of Big Data and Deep Learning, there is a common view that machine learning approaches are the only way to cope with the robust and scalable information extraction and summarization.
2 code implementations • SEMEVAL 2016 • Guntis Barzdins, Didzis Gosko
The first extension com-bines the smatch scoring script with the C6. 0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs.
1 code implementation • LREC 2016 • Guntis Barzdins, Steve Renals, Didzis Gosko
The results of this paper describe a novel approach to the automatic story segmentation and storyline clustering problem.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
no code implementations • 20 Nov 2015 • Normunds Gruzitis, Guntis Barzdins
We show that micro-ontologies and multi-word units allow integration of the rich and polysemous multi-domain background knowledge into CNL thus providing interpretation for the content words.
no code implementations • 26 Jun 2014 • Normunds Gruzitis, Peteris Paikens, Guntis Barzdins
In this paper we are focusing on verbs, investigating the possibility of creating a multilingual FrameNet-based GF library.
no code implementations • 10 Jun 2014 • Guntis Barzdins
The paper presents a FrameNet-based information extraction and knowledge representation framework, called FrameNet-CNL.
no code implementations • LREC 2014 • Guntis Barzdins, Didzis Gosko, Laura Rituma, Peteris Paikens
Frame-semantic parsing is a kind of automatic semantic role labeling performed according to the FrameNet paradigm.