Guntis Barzdins

Also published as: Guntis Bārzdiņš


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RIGA at SemEval-2023 Task 2: NER Enhanced with GPT-3
Eduards Mukans | Guntis Barzdins
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

The following is a description of the RIGA team’s submissions for the English track of the SemEval-2023 Task 2: Multilingual Complex Named Entity Recognition (MultiCoNER) II. Our approach achieves 17% boost in results by utilizing pre-existing Large-scale Language Models (LLMs), such as GPT-3, to gather additional contexts. We then fine-tune a pre-trained neural network utilizing these contexts. The final step of our approach involves meticulous model and compute resource scaling, which results in improved performance. Our results placed us 12th out of 34 teams in terms of overall ranking and 7th in terms of the noisy subset ranking. The code for our method is available on GitHub (


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Latvian National Corpora Collection –
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
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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. The collection is intended to cover diverse Latvian language use cases and all the important text types and genres (e.g. news, social media, blogs, books, scientific texts, debates, essays, etc.), taking into account both quality and size aspects. To reach this objective, LNCC is a continuous multi-institutional and multi-project effort, supported by the Digital Humanities and Language Technology communities in Latvia. LNCC includes a broad range of Latvian texts from the Latvian National Library, Culture Information Systems Centre, Latvian National News Agency, Latvian Parliament, Latvian web crawl, various Latvian publishers, and from the Latvian language corpora created by Institute of Mathematics and Computer Science and its partners, including spoken language corpora. All corpora of LNCC are re-annotated with a uniform morpho-syntactic annotation scheme which enables federated search and consistent linguistics analysis in all the LNCC corpora, as well as facilitates to select and mix various corpora for pre-training large Latvian language models like BERT and GPT.

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RIGA at SemEval-2022 Task 1: Scaling Recurrent Neural Networks for CODWOE Dictionary Modeling
Eduards Mukans | Gus Strazds | Guntis Barzdins
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

Described are our two entries “emukans” and “guntis” for the definition modeling track of CODWOE SemEval-2022 Task 1. Our approach is based on careful scaling of a GRU recurrent neural network, which exhibits double descent of errors, corresponding to significant improvements also per human judgement. Our results are in the middle of the ranking table per official automatic metrics.


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Integrating Multiple NLP Technologies into an Open-source Platform for Multilingual Media Monitoring
Ulrich Germann | Renārs Liepins | Didzis Gosko | Guntis Barzdins
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

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. It assembles numerous state-of-the-art NLP technologies into a fully automated media ingestion pipeline that can record live broadcasts, detect and transcribe spoken content, translate from several languages (original text or transcribed speech) into English, recognize Named Entities, detect topics, cluster and summarize documents across language barriers, and extract and store factual claims in these news items. This paper describes the intended use cases and discusses the system design decisions that allowed us to integrate state-of-the-art NLP modules into an effective workflow with comparatively little effort.

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Multilingual Clustering of Streaming News
Sebastião Miranda | Artūrs Znotiņš | Shay B. Cohen | Guntis Barzdins
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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 clusters. Unlike typical clustering approaches that report results on datasets with 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. In our formulation, the monolingual clusters group together documents while the crosslingual clusters group together monolingual clusters, one per language that appears in the stream. Our method is simple to implement, computationally efficient and produces state-of-the-art results on datasets in German, English and Spanish.

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The SUMMA Platform: Scalable Understanding of Multilingual Media
Ulrich Germann | Peggy van der Kreeft | Guntis Barzdins | Alexandra Birch
Proceedings of the 21st Annual Conference of the European Association for Machine Translation

We present the latest version of the SUMMA platform, an open-source software platform for monitoring and interpreting multi-lingual media, from written news published on the internet to live media broadcasts via satellite or internet streaming.

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The SUMMA Platform: A Scalable Infrastructure for Multi-lingual Multi-media Monitoring
Ulrich Germann | Renārs Liepins | Guntis Barzdins | Didzis Gosko | Sebastião Miranda | David Nogueira
Proceedings of ACL 2018, System Demonstrations

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. The Platform offers a fully automated media ingestion pipeline capable of recording live broadcasts, detection and transcription of spoken content, translation of all text (original or transcribed) into English, recognition and linking of Named Entities, topic detection, clustering and cross-lingual multi-document summarization of related media items, and last but not least, extraction and storage of factual claims in these news items. Browser-based graphical user interfaces provide humans with aggregated information as well as structured access to individual news items stored in the Platform’s database. This paper describes the intended use cases and provides an overview over the system’s implementation.


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RIGOTRIO at SemEval-2017 Task 9: Combining Machine Learning and Grammar Engineering for AMR Parsing and Generation
Normunds Gruzitis | Didzis Gosko | Guntis Barzdins
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

By addressing both text-to-AMR parsing and AMR-to-text generation, SemEval-2017 Task 9 established AMR as a powerful semantic interlingua. We strengthen the interlingual aspect of AMR by applying the multilingual Grammatical Framework (GF) for AMR-to-text generation. Our current rule-based GF approach completely covered only 12.3% of the test AMRs, therefore we combined it with state-of-the-art JAMR Generator to see if the combination increases or decreases the overall performance. The combined system achieved the automatic BLEU score of 18.82 and the human Trueskill score of 107.2, to be compared to the plain JAMR Generator results. As for AMR parsing, we added NER extensions to our SemEval-2016 general-domain AMR parser to handle the biomedical genre, rich in organic compound names, achieving Smatch F1=54.0%.

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The SUMMA Platform Prototype
Renars Liepins | Ulrich Germann | Guntis Barzdins | Alexandra Birch | Steve Renals | Susanne Weber | Peggy van der Kreeft | Hervé Bourlard | João Prieto | Ondřej Klejch | Peter Bell | Alexandros Lazaridis | 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 | Sebastião Miranda | 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
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.


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Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
Guntis Barzdins | Steve Renals | Didzis Gosko
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

The paper steps outside the comfort-zone of the traditional NLP tasks like automatic speech recognition (ASR) and machine translation (MT) to addresses two novel problems arising in the automated multilingual news monitoring: segmentation of the TV and radio program ASR transcripts into individual stories, and clustering of the individual stories coming from various sources and languages into storylines. Storyline clustering of stories covering the same events is an essential task for inquisitorial media monitoring. We address these two problems jointly by engaging the low-dimensional semantic representation capabilities of the sequence to sequence neural translation models. To enable joint multi-task learning for multilingual neural translation of morphologically rich languages we replace the attention mechanism with the sliding-window mechanism and operate the sequence to sequence neural translation model on the character-level rather than on the word-level. The story segmentation and storyline clustering problem is tackled by examining the low-dimensional vectors produced as a side-product of the neural translation process. The results of this paper describe a novel approach to the automatic story segmentation and storyline clustering problem.

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RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
Guntis Barzdins | Didzis Gosko
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


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Riga: from FrameNet to Semantic Frames with C6.0 Rules
Guntis Barzdins | Peteris Paikens | Didzis Gosko
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)


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Using C5.0 and Exhaustive Search for Boosting Frame-Semantic Parsing Accuracy
Guntis Barzdins | Didzis Gosko | Laura Rituma | Peteris Paikens
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Frame-semantic parsing is a kind of automatic semantic role labeling performed according to the FrameNet paradigm. The paper reports a novel approach for boosting frame-semantic parsing accuracy through the use of the C5.0 decision tree classifier, a commercial version of the popular C4.5 decision tree classifier, and manual rule enhancement. Additionally, the possibility to replace C5.0 by an exhaustive search based algorithm (nicknamed C6.0) is described, leading to even higher frame-semantic parsing accuracy at the expense of slightly increased training time. The described approach is particularly efficient for languages with small FrameNet annotated corpora as it is for Latvian, which is used for illustration. Frame-semantic parsing accuracy achieved for Latvian through the C6.0 algorithm is on par with the state-of-the-art English frame-semantic parsers. The paper includes also a frame-semantic parsing use-case for extracting structured information from unstructured newswire texts, sometimes referred to as bridging of the semantic gap.


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Towards a More Natural Multilingual Controlled Language Interface to OWL
Normunds Gruzitis | Guntis Barzdins
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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Invited Paper: When FrameNet meets a Controlled Natural Language
Guntis Bārzdiņš
Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011)


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Dependency-Based Hybrid Model of Syntactic Analysis for the Languages with a Rather Free Word Order
Guntis Bārzdiņš | Normunds Grūzītis | Gunta Nešpore | Baiba Saulīte
Proceedings of the 16th Nordic Conference of Computational Linguistics (NODALIDA 2007)