FrameNet CNL: a Knowledge Representation and Information Extraction Language

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 FrameNet-CNL eventually could shape the natural language subset used for writing the newswire articles.Comment: CNL-2014 camera-ready version. The final publication is available at link.springer.co

Gender-specific Machine Translation with Large Language Models

Decoder-only Large Language Models (LLMs) have demonstrated potential in machine translation (MT), albeit with performance slightly lagging behind traditional encoder-decoder Neural Machine Translation (NMT) systems. However, LLMs offer a unique advantage: the ability to control the properties of the output through prompts. In this study, we harness this flexibility to explore LLaMa's capability to produce gender-specific translations for languages with grammatical gender. Our results indicate that LLaMa can generate gender-specific translations with competitive accuracy and gender bias mitigation when compared to NLLB, a state-of-the-art multilingual NMT system. Furthermore, our experiments reveal that LLaMa's translations are robust, showing significant performance drops when evaluated against opposite-gender references in gender-ambiguous datasets but maintaining consistency in less ambiguous contexts. This research provides insights into the potential and challenges of using LLMs for gender-specific translations and highlights the importance of in-context learning to elicit new tasks in LLMs

Evaluation of contextual embeddings on less-resourced languages

The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives. Most existing work focuses on English; in contrast, we present here the first multilingual empirical comparison of two ELMo and several monolingual and multilingual BERT models using 14 tasks in nine languages. In monolingual settings, our analysis shows that monolingual BERT models generally dominate, with a few exceptions such as the dependency parsing task, where they are not competitive with ELMo models trained on large corpora. In cross-lingual settings, BERT models trained on only a few languages mostly do best, closely followed by massively multilingual BERT models

Conference Program

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), xi-xiv. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206

Contents

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), iii-vi. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206

Towards Multilingual Coreference Resolution

The current work investigates the problems that occur when coreference resolution is considered as a multilingual task. We assess the issues that arise when a framework using the mention-pair coreference resolution model and memory-based learning for the resolution process are used. Along the way, we revise three essential subtasks of coreference resolution: mention detection, mention head detection and feature selection. For each of these aspects we propose various multilingual solutions including both heuristic, rule-based and machine learning methods. We carry out a detailed analysis that includes eight different languages (Arabic, Catalan, Chinese, Dutch, English, German, Italian and Spanish) for which datasets were provided by the only two multilingual shared tasks on coreference resolution held so far: SemEval-2 and CoNLL-2012. Our investigation shows that, although complex, the coreference resolution task can be targeted in a multilingual and even language independent way. We proposed machine learning methods for each of the subtasks that are affected by the transition, evaluated and compared them to the performance of rule-based and heuristic approaches. Our results confirmed that machine learning provides the needed flexibility for the multilingual task and that the minimal requirement for a language independent system is a part-of-speech annotation layer provided for each of the approached languages. We also showed that the performance of the system can be improved by introducing other layers of linguistic annotations, such as syntactic parses (in the form of either constituency or dependency parses), named entity information, predicate argument structure, etc. Additionally, we discuss the problems occurring in the proposed approaches and suggest possibilities for their improvement

The META-NORD language reports

Proceedings of the NODALIDA 2011 Workshop Visibility and Availability of LT Resources. Editors: Sjur Nørstebø Moshagen and Per Langgård. NEALT Proceedings Series, Vol. 13 (2011), 23–27. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/1697

Promocijas darbs

Elektroniskā versija nesatur pielikumusPromocijas darbs veltīts hibrīda latviešu valodas gramatikas modeļa izstrādei un transformēšanai uz Universālo atkarību (Universal Dependencies, UD) modeli. Promocijas darbā ir aizsākts jauns latviešu valodas izpētes virziens – sintaktiski marķētos tekstos balstīti pētījumi. Darba rezultātā ir izstrādāts un aprobēts fundamentāls, latviešu valodai iepriekš nebijis valodas resurss – mašīnlasāms sintaktiski marķēts korpuss 17 tūkstošu teikumu apmērā. Teikumi ir marķēti atbilstoši diviem dažādiem sintaktiskās marķēšanas modeļiem – darbā radītajam frāžu struktūru un atkarību gramatikas hibrīdam un starptautiski aprobētajam UD modelim. Izveidotais valodas resurss publiski pieejams gan lejuplādei, gan tiešsaistes meklēšanai abos iepriekš minētajos marķējuma veidos. Pētījuma laikā radīta rīku kopa un latviešu valodas sintaktiski marķētā korpusa veidošanai vajadzīgā infrastruktūra. Tajā skaitā tika definēti plašam valodas pārklājumam nepieciešamie LU MII eksperimentālā hibrīdā gramatikas modeļa paplašinājumi. Tāpat tika analizētas iespējas atbilstoši hibrīdmodelim marķētus datus pārveidot uz atkarību modeli, un tika radīts atvasināts UD korpuss. Izveidotais sintaktiski marķētais korpuss ir kalpojis par pamatu, lai varētu radīt augstas precizitātes (91%) parsētājus latviešu valodai. Savukārt dalība UD iniciatīvā ir veicinājusi latviešu valodas un arī citu fleksīvu valodu resursu starptautisko atpazīstamību un fleksīvām valodām piemērotāku rīku izveidi datorlingvistikā – pētniecības jomā, kuras vēsturiskā izcelsme pamatā meklējama darbā ar analītiskajām valodām. Atslēgvārdi: sintakses korpuss, Universal Dependencies, valodu tehnoloģijasThe given doctoral thesis describes the creation of a hybrid grammar model for the Latvian language, as well as its subsequent conversion to a Universal Dependencies (UD) grammar model. The thesis also lays the groundwork for Latvian language research through syntactically annotated texts. In this work, a fundamental Latvian language resource was developed and evaluated for the first time – a machine-readable treebank of 17 thousand syntactically annotated sentences. The sentences are annotated according to two syntactic annotation models: the hybrid grammar model developed in the thesis, and the internationally recognised UD model. Both annotated versions of the treebank are publicly available for downloading or querying online. Over the course of the study, a set of tools and infrastructure necessary for treebank creation and maintenance were developed. The language coverage of the IMCS UL experimental hybrid model was extended, and the possibilities were defined for converting data annotated according to the hybrid grammar model to the dependency grammar model. Based on this work, a derived UD treebank was created. The resulting treebank has served as a basis for the development of high accuracy (91%) Latvian language parsers. Furthermore, the participation in the UD initiative has promoted the international recognition of Latvian and other inflective languages and the development of better-fitted tools for inflective language processing in computational linguistics, which historically has been more oriented towards analytic languages. Keywords: treebank, Universal Dependencies, language technologie

Eesti keele üldvaldkonna tekstide laia kattuvusega automaatne sündmusanalüüs

Seoses tekstide suuremahulise digitaliseerimisega ning digitaalse tekstiloome järjest laiema levikuga on tohutul hulgal loomuliku keele tekste muutunud ja muutumas masinloetavaks. Masinloetavus omab potentsiaali muuta tekstimassiivid inimeste jaoks lihtsamini hallatavaks, nt lubada rakendusi nagu automaatne sisukokkuvõtete tegemine ja tekstide põhjal küsimustele vastamine, ent paraku ei ulatu praegused automaatanalüüsi võimalused tekstide sisu tegeliku mõistmiseni. Oletatakse, tekstide sisu mõistvale automaatanalüüsile viib meid lähemale sündmusanalüüs – kuna paljud tekstid on narratiivse ülesehitusega, tõlgendatavad kui „sündmuste kirjeldused”, peaks tekstidest sündmuste eraldamine ja formaalsel kujul esitamine pakkuma alust mitmete „teksti mõistmist” nõudvate keeletehnoloogia rakenduste loomisel. Käesolevas väitekirjas uuritakse, kuivõrd saab eestikeelsete tekstide sündmusanalüüsi käsitleda kui avatud sündmuste hulka ja üldvaldkonna tekste hõlmavat automaatse lingvistilise analüüsi ülesannet. Probleemile lähenetakse eesti keele automaatanalüüsi kontekstis uudsest, sündmuste ajasemantikale keskenduvast perspektiivist. Töös kohandatakse eesti keelele TimeML märgendusraamistik ja luuakse raamistikule toetuv automaatne ajaväljendite tuvastaja ning ajasemantilise märgendusega (sündmusviidete, ajaväljendite ning ajaseoste märgendusega) tekstikorpus; analüüsitakse korpuse põhjal inimmärgendajate kooskõla sündmusviidete ja ajaseoste määramisel ning lõpuks uuritakse võimalusi ajasemantika-keskse sündmusanalüüsi laiendamiseks geneeriliseks sündmusanalüüsiks sündmust väljendavate keelendite samaviitelisuse lahendamise näitel. Töö pakub suuniseid tekstide ajasemantika ja sündmusstruktuuri märgenduse edasiarendamiseks tulevikus ning töös loodud keeleressurssid võimaldavad nii konkreetsete lõpp-rakenduste (nt automaatne ajaküsimustele vastamine) katsetamist kui ka automaatsete märgendustööriistade edasiarendamist.  Due to massive scale digitalisation processes and a switch from traditional means of written communication to digital written communication, vast amounts of human language texts are becoming machine-readable. Machine-readability holds a potential for easing human effort on searching and organising large text collections, allowing applications such as automatic text summarisation and question answering. However, current tools for automatic text analysis do not reach for text understanding required for making these applications generic. It is hypothesised that automatic analysis of events in texts leads us closer to the goal, as many texts can be interpreted as stories/narratives that are decomposable into events. This thesis explores event analysis as broad-coverage and general domain automatic language analysis problem in Estonian, and provides an investigation starting from time-oriented event analysis and tending towards generic event analysis. We adapt TimeML framework to Estonian, and create an automatic temporal expression tagger and a news corpus manually annotated for temporal semantics (event mentions, temporal expressions, and temporal relations) for the language; we analyse consistency of human annotation of event mentions and temporal relations, and, finally, provide a preliminary study on event coreference resolution in Estonian news. The current work also makes suggestions on how future research can improve Estonian event and temporal semantic annotation, and the language resources developed in this work will allow future experimentation with end-user applications (such as automatic answering of temporal questions) as well as provide a basis for developing automatic semantic analysis tools