Jack Rueter


2022

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Using Graph-Based Methods to Augment Online Dictionaries of Endangered Languages
Khalid Alnajjar | Mika Hämäläinen | Niko Tapio Partanen | Jack Rueter
Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

Many endangered Uralic languages have multilingual machine readable dictionaries saved in an XML format. However, the dictionaries cover translations very inconsistently between language pairs, for instance, the Livonian dictionary has some translations to Finnish, Latvian and Estonian, and the Komi-Zyrian dictionary has some translations to Finnish, English and Russian. We utilize graph-based approaches to augment such dictionaries by predicting new translations to existing and new languages based on different dictionaries for endangered languages and Wiktionaries. Our study focuses on the lexical resources for Komi-Zyrian (kpv), Erzya (myv) and Livonian (liv). We evaluate our approach by human judges fluent in the three endangered languages in question. Based on the evaluation, the method predicted good or acceptable translations 77% of the time. Furthermore, we train a neural prediction model to predict the quality of the automatically predicted translations with an 81% accuracy. The resulting extensions to the dictionaries are made available on the online dictionary platform used by the speakers of these languages.

2021

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Numerals and what counts
Jack Rueter | Niko Partanen | Flammie A. Pirinen
Proceedings of the Fifth Workshop on Universal Dependencies (UDW, SyntaxFest 2021)

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Linguistic change and historical periodization of Old Literary Finnish
Niko Partanen | Khalid Alnajjar | Mika Hämäläinen | Jack Rueter
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

In this study, we have normalized and lemmatized an Old Literary Finnish corpus using a lemmatization model trained on texts from Agricola. We analyse the error types that occur and appear in different decades, and use word error rate (WER) and different error types as a proxy for measuring linguistic innovation and change. We show that the proposed approach works, and the errors are connected to accumulating changes and innovations, which also results in a continuous decrease in the accuracy of the model. The described error types also guide further work in improving these models, and document the currently observed issues. We also have trained word embeddings for four centuries of lemmatized Old Literary Finnish, which are available on Zenodo.

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Apurinã Universal Dependencies Treebank
Jack Rueter | Marília Fernanda Pereira de Freitas | Sidney Da Silva Facundes | Mika Hämäläinen | Niko Partanen
Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas

This paper presents and discusses the first Universal Dependencies treebank for the Apurinã language. The treebank contains 76 fully annotated sentences, applies 14 parts-of-speech, as well as seven augmented or new features — some of which are unique to Apurinã. The construction of the treebank has also served as an opportunity to develop finite-state description of the language and facilitate the transfer of open-source infrastructure possibilities to an endangered language of the Amazon. The source materials used in the initial treebank represent fieldwork practices where not all tokens of all sentences are equally annotated. For this reason, establishing regular annotation practices for the entire Apurinã treebank is an ongoing project.

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Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline
Mika Hämäläinen | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda

This study presents a new dataset on rumor detection in Finnish language news headlines. We have evaluated two different LSTM based models and two different BERT models, and have found very significant differences in the results. A fine-tuned FinBERT reaches the best overall accuracy of 94.3% and rumor label accuracy of 96.0% of the time. However, a model fine-tuned on Multilingual BERT reaches the best factual label accuracy of 97.2%. Our results suggest that the performance difference is due to a difference in the original training data. Furthermore, we find that a regular LSTM model works better than one trained with a pretrained word2vec model. These findings suggest that more work needs to be done for pretrained models in Finnish language as they have been trained on small and biased corpora.

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Overview of Open-Source Morphology Development for the Komi-Zyrian Language: Past and future
Jack Rueter | Niko Partanen | Mika Hämäläinen | Trond Trosterud
Proceedings of the Seventh International Workshop on Computational Linguistics of Uralic Languages

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Detecting Depression in Thai Blog Posts: a Dataset and a Baseline
Mika Hämäläinen | Pattama Patpong | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53% accuracy with a Thai BERT model in detecting depression. This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia. Our corpus, code and trained models have been released openly on Zenodo.

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Finnish Dialect Identification: The Effect of Audio and Text
Mika Hämäläinen | Khalid Alnajjar | Niko Partanen | Jack Rueter
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Finnish is a language with multiple dialects that not only differ from each other in terms of accent (pronunciation) but also in terms of morphological forms and lexical choice. We present the first approach to automatically detect the dialect of a speaker based on a dialect transcript and transcript with audio recording in a dataset consisting of 23 different dialects. Our results show that the best accuracy is received by combining both of the modalities, as text only reaches to an overall accuracy of 57%, where as text and audio reach to 85%. Our code, models and data have been released openly on Github and Zenodo.

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Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered
Mika Hämäläinen | Niko Partanen | Jack Rueter | Khalid Alnajjar
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.

2020

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Ve’rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement
Khalid Alnajjar | Mika Hämäläinen | Jack Rueter | Niko Partanen
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

We present an open-source online dictionary editing system, Ve′rdd, that offers a chance to re-evaluate and edit grassroots dictionaries that have been exposed to multiple amateur editors. The idea is to incorporate community activities into a state-of-the-art finite-state language description of a seriously endangered minority language, Skolt Sami. Problems involve getting the community to take part in things above the pencil-and-paper level. At times, it seems that the native speakers and the dictionary oriented are lacking technical understanding to utilize the infrastructures which might make their work more meaningful in the future, i.e. multiple reuse of all of their input. Therefore, our system integrates with the existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.

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Open-Source Morphology for Endangered Mordvinic Languages
Jack Rueter | Mika Hämäläinen | Niko Partanen
Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)

This document describes shared development of finite-state description of two closely related but endangered minority languages, Erzya and Moksha. It touches upon morpholexical unity and diversity of the two languages and how this provides a motivation for shared open-source FST development. We describe how we have designed the transducers so that they can benefit from existing open-source infrastructures and are as reusable as possible.

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On the questions in developing computational infrastructure for Komi-Permyak
Jack Rueter | Niko Partanen | Larisa Ponomareva
Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages

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On Editing Dictionaries for Uralic Languages in an Online Environment
Khalid Alnajjar | Mika Hämäläinen | Jack Rueter
Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages

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FST Morphology for the Endangered Skolt Sami Language
Jack Rueter | Mika Hämäläinen
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

We present advances in the development of a FST-based morphological analyzer and generator for Skolt Sami. Like other minority Uralic languages, Skolt Sami exhibits a rich morphology, on the one hand, and there is little golden standard material for it, on the other. This makes NLP approaches for its study difficult without a solid morphological analysis. The language is severely endangered and the work presented in this paper forms a part of a greater whole in its revitalization efforts. Furthermore, we intersperse our description with facilitation and description practices not well documented in the infrastructure. Currently, the analyzer covers over 30,000 Skolt Sami words in 148 inflectional paradigms and over 12 derivational forms.

2019

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Revisiting NMT for Normalization of Early English Letters
Mika Hämäläinen | Tanja Säily | Jack Rueter | Jörg Tiedemann | Eetu Mäkelä
Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

This paper studies the use of NMT (neural machine translation) as a normalization method for an early English letter corpus. The corpus has previously been normalized so that only less frequent deviant forms are left out without normalization. This paper discusses different methods for improving the normalization of these deviant forms by using different approaches. Adding features to the training data is found to be unhelpful, but using a lexicographical resource to filter the top candidates produced by the NMT model together with lemmatization improves results.

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Finding Sami Cognates with a Character-Based NMT Approach
Mika Hämäläinen | Jack Rueter
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

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Morphosyntactic Disambiguation in an Endangered Language Setting
Jeff Ens | Mika Hämäläinen | Jack Rueter | Philippe Pasquier
Proceedings of the 22nd Nordic Conference on Computational Linguistics

Endangered Uralic languages present a high variety of inflectional forms in their morphology. This results in a high number of homonyms in inflections, which introduces a lot of morphological ambiguity in sentences. Previous research has employed constraint grammars to address this problem, however CGs are often unable to fully disambiguate a sentence, and their development is labour intensive. We present an LSTM based model for automatically ranking morphological readings of sentences based on their quality. This ranking can be used to evaluate the existing CG disambiguators or to directly morphologically disambiguate sentences. Our approach works on a morphological abstraction and it can be trained with a very small dataset.

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Survey of Uralic Universal Dependencies development
Niko Partanen | Jack Rueter
Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019)

2018

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Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages
Mika Hämäläinen | Liisa Lotta Tarvainen | Jack Rueter
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages
Tommi A. Pirinen | Michael Rießler | Jack Rueter | Trond Trosterud | Francis M. Tyers
Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages

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Development of an Open Source Natural Language Generation Tool for Finnish
Mika Hämäläinen | Jack Rueter
Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages

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Towards an open-source universal-dependency treebank for Erzya
Jack Rueter | Francis Tyers
Proceedings of the Fourth International Workshop on Computational Linguistics of Uralic Languages

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Normalizing Early English Letters to Present-day English Spelling
Mika Hämäläinen | Tanja Säily | Jack Rueter | Jörg Tiedemann | Eetu Mäkelä
Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

This paper presents multiple methods for normalizing the most deviant and infrequent historical spellings in a corpus consisting of personal correspondence from the 15th to the 19th century. The methods include machine translation (neural and statistical), edit distance and rule-based FST. Different normalization methods are compared and evaluated. All of the methods have their own strengths in word normalization. This calls for finding ways of combining the results from these methods to leverage their individual strengths.

2017

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Building and using language resources and infrastructure to develop e-learning programs for a minority language
Heli Uibo | Jack Rueter | Sulev Iva
Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition

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Synchronized Mediawiki based analyzer dictionary development
Jack Rueter | Mika Hämäläinen
Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages

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DEMO: Giellatekno Open-source click-in-text dictionaries for bringing closely related languages into contact.
Jack Rueter
Proceedings of the Third Workshop on Computational Linguistics for Uralic Languages

2015

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Oahpa! Õpi! Opiq! Developing free online programs for learning Estonian and Võro
Heli Uibo | Jaak Pruulmann-Vengerfeldt | Jack Rueter | Sulev Iva
Proceedings of the fourth workshop on NLP for computer-assisted language learning