Analyse syntaxique de langues faiblement dotées à partir de plongements de mots multilingues: Application au same du nord et au komi-zyriène

International audienceThis article presents an attempt to apply efficient parsing methods based on recur- sive neural networks to languages for which very few resources are available. We propose an original approach based on multilingual word embeddings acquired from different languages so as to determine the best language combination for learning. The approach yields competitive results in contexts considered as linguistically difficult.Cet article présente une tentative pour appliquer des méthodes d'analyse syntaxique performantes, à base de réseaux de neurones récursifs, à des langues pour lesquelles on dispose de très peu de ressources. Nous proposons une méthode originale à base de plongements de mots multilingues obtenus à partir de langues plus ou moins proches typologiquement, afin de déterminer la meilleure combinaison de langues possibles pour l'apprentissage. L'approche a permis d'obtenir des résultats encourageants dans des contextes considérés comme linguisti-quement difficiles. Le code source est disponible en ligne (voir https://github.com/jujbob)

Yet Another Format of Universal Dependencies for Korean

In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies. We present the linguistic rationale that illustrates the motivation and the necessity of adopting the morpheme-based format, and develop scripts that convert between the original format used by Universal Dependencies and the proposed morpheme-based format automatically. The effectiveness of the proposed format for Korean dependency parsing is then testified by both statistical and neural models, including UDPipe and Stanza, with our carefully constructed morpheme-based word embedding for Korean. morphUD outperforms parsing results for all Korean UD treebanks, and we also present detailed error analyses.Comment: COLING2022, Poste

The first Komi-Zyrian Universal Dependencies treebanks

Partanen N, Blokland R, Lim KT, Poibeau T, Rießler M. The first Komi-Zyrian Universal Dependencies treebanks. Presented at the 2018 Conference on Empirical Methods in Natural Language Processing (Universal Dependencies Workshop 2018), Brussels

A functional genetic toolbox for human tissue-derived organoids.

Funder: Alzheimers Research UK Stem Cell Research CentreHuman organoid systems recapitulate key features of organs offering platforms for modelling developmental biology and disease. Tissue-derived organoids have been widely used to study the impact of extrinsic niche factors on stem cells. However, they are rarely used to study endogenous gene function due to the lack of efficient gene manipulation tools. Previously, we established a human foetal lung organoid system (Nikolić et al., 2017). Here, using this organoid system as an example we have systematically developed and optimised a complete genetic toolbox for use in tissue-derived organoids. This includes 'Organoid Easytag' our efficient workflow for targeting all types of gene loci through CRISPR-mediated homologous recombination followed by flow cytometry for enriching correctly-targeted cells. Our toolbox also incorporates conditional gene knock-down or overexpression using tightly-inducible CRISPR interference and CRISPR activation which is the first efficient application of these techniques to tissue-derived organoids. These tools will facilitate gene perturbation studies in tissue-derived organoids facilitating human disease modelling and providing a functional counterpart to many on-going descriptive studies, such as the Human Cell Atlas Project

Dependency parsing of code-switching data with cross-lingual feature representations

Partanen N, KyungTae L, Rießler M, Poibeau T. Dependency parsing of code-switching data with cross-lingual feature representations. In: Pirinen TA, Rießler M, Rueter J, Trosterud T, Tyers FM, eds. Proceedings of the 4th International Workshop for Computational Linguistics for Uralic Languages. Helsinki: Association for Computational Linguistics; 2018: 1-17

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr

Méthodes d’amorçage pour l’analyse en dépendances de langues peu dotées

Dependency parsing is an essential component of several NLP applications owing its ability to capture complex relational information in a sentence. Due to the wider availability of dependency treebanks, most dependency parsing systems are built using supervised learning techniques. These systems require a significant amount of annotated data and are thus targeted toward specific languages for which this type of data are available. Unfortunately, producing sufficient annotated data for low-resource languages is time- and resource-consuming. To address the aforementioned issue, the present study investigates three bootstrapping methods, namely, (1) multi-lingual transfer learning, (2) deep contextualized embedding, and (3) Co-training. Multi-lingual transfer learning is a typical supervised learning approach that can transfer dependency knowledge using multi-lingual training data based on multi-lingual lexical representations. Deep contextualized embedding maximizes the use of lexical features during supervised learning based on enhanced sub-word representations and language model (LM). Lastly, co-training is a semi-supervised learning method that leverages parsing accuracies using unlabeled data. Our approaches have the advantage of requiring only a small bilingual dictionary or easily obtainable unlabeled resources (e.g., Wikipedia) to improve parsing accuracy in low-resource conditions. We evaluated our parser on 57 official CoNLL shared task languages as well as on Komi, which is a language we developed as a training and evaluation corpora for low-resource scenarios. The evaluation results demonstrated outstanding performances of our approaches in both low- and high-resource dependency parsing in the 2017 and 2018 CoNLL shared tasks. A survey of both model transfer learning and semi-supervised methods for low-resource dependency parsing was conducted, where the effect of each method under different conditions was extensively investigated.L'analyse en dépendances est une composante essentielle de nombreuses applications de TAL (Traitement Automatique des Langues), dans la mesure où il s'agit de fournir une analyse des relations entre les principaux éléments de la phrase. La plupart des systèmes d'analyse en dépendances sont issus de techniques d'apprentissage supervisées, à partir de grands corpus annotés. Ce type d'analyse est dès lors limité à quelques langues seulement, qui disposent des ressources adéquates. Pour les langues peu dotées, la production de données annotées est une tâche impossible le plus souvent, faute de moyens et d'annotateurs disponibles. Afin de résoudre ce problème, la thèse examine trois méthodes d’amorçage, à savoir (1) l’apprentissage par transfert multilingue, (2) les plongements vectoriels contextualisés profonds et (3) le co-entrainement. La première idée, l'apprentissage par transfert multilingue, permet de transférer des connaissances d'une langue pour laquelle on dispose de nombreuses ressources, et donc de traitements efficaces, vers une langue peu dotée. Les plongements vectoriels contextualisés profonds, quant à eux, permettent une représentation optimale du sens des mots en contexte, grâce à la notion de modèle de langage. Enfin, le co-entrainement est une méthode d'apprentissage semi-supervisée, qui permet d'améliorer les performances des systèmes en utilisant les grandes quantités de données non annotées souvent disponibles pour les différentes langues visées. Nos approches ne nécessitent qu'un petit dictionnaire bilingue ou des ressources non étiquetées faciles à obtenir (à partir de Wikipedia par exemple) pour améliorer la précision de l'analyse pour des langues où les ressources disponibles sont insuffisantes. Nous avons évalué notre analyseur syntaxique sur 57 langues à travers la participation aux campagnes d'évaluation proposées dans le cadre de la conférence CoNLL. Nous avons également mené des expériences sur d'autres langues, comme le komi, une langue finno-ougrienne parlée en Russie : le komi offre un scénario réaliste pour tester les idées mises en avant dans la thèse. Notre système a obtenu des résultats très compétitifs lors de campagnes d'évaluation officielles, notamment lors des campagnes CoNLL 2017 et 2018. Cette thèse offre donc des perspectives intéressantes pour le traitement automatique des langues peu dotées, un enjeu majeur pour le TAL dans les années à venir

Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian

International audienceThe paper presents a method for parsing low-resource languages with very small training corpora using multilingual word embeddings and annotated corpora of larger languages. The study demonstrates that specific language combinations enable improved dependency parsing when compared to previous work, allowing for wider reuse of pre-existing resources when parsing low-resource languages. The study also explores the question of whether contemporary contact languages or genetically related languages would be the most fruitful starting point for multilingual parsing scenarios

Analyse syntaxique de langues faiblement dotées à partir de plongements de mots multilingues: Application au same du nord et au komi-zyriène

International audienceThis article presents an attempt to apply efficient parsing methods based on recur- sive neural networks to languages for which very few resources are available. We propose an original approach based on multilingual word embeddings acquired from different languages so as to determine the best language combination for learning. The approach yields competitive results in contexts considered as linguistically difficult.Cet article présente une tentative pour appliquer des méthodes d'analyse syntaxique performantes, à base de réseaux de neurones récursifs, à des langues pour lesquelles on dispose de très peu de ressources. Nous proposons une méthode originale à base de plongements de mots multilingues obtenus à partir de langues plus ou moins proches typologiquement, afin de déterminer la meilleure combinaison de langues possibles pour l'apprentissage. L'approche a permis d'obtenir des résultats encourageants dans des contextes considérés comme linguisti-quement difficiles. Le code source est disponible en ligne (voir https://github.com/jujbob)

Multilingual Dependency Parsing for Low-Resource Languages: Case Studies on North Saami and Komi-Zyrian

International audienceThe paper presents a method for parsing low-resource languages with very small training corpora using multilingual word embeddings and annotated corpora of larger languages. The study demonstrates that specific language combinations enable improved dependency parsing when compared to previous work, allowing for wider reuse of pre-existing resources when parsing low-resource languages. The study also explores the question of whether contemporary contact languages or genetically related languages would be the most fruitful starting point for multilingual parsing scenarios