NoDaLiDa 2023 - May 22-24, 2023


Estonian Named Entity Recognition: New Datasets and Models

Kairit Sirts

This paper presents the annotation process of two Estonian named entity recognition (NER) datasets, involving the creation of annotation guidelines for labeling eleven different types of entities. In addition to the commonly annotated entities such as person names, organization names, and locations, the annotation scheme encompasses geopolitical entities, product names, titles/roles, events, dates, times, monetary values, and percents. The annotation was performed on two datasets, one involving reannotating an existing NER dataset primarily composed of news texts and the other incorporating new texts from news and social media domains. Transformer-based models were trained on these annotated datasets to establish baseline predictive performance. Our findings indicate that the best results were achieved by training a single model on the combined dataset, suggesting that the domain differences between the datasets are relatively small. 

The finer they get: Aggregating fine-tuned models improves lexical semantic change detection

Wei Zhou, Nina Tahmasebi, Haim Dubossarsky

In this work we investigate the hypothesis that enriching contextualized models using fine-tuning tasks can improve their capacity to detect lexical semantic change (LSC). We include tasks  aimed to capture both low-level linguistic information like part-of-speech tagging, as well as higher level (semantic) information.

Through a series of analyses we demonstrate that certain combinations of fine-tuning tasks, like sentiment, syntactic information, and logical inference, bring large improvements to standard LSC models that are based only on standard language modeling. We test on the binary classification and ranking tasks of SemEval-2020 Task 1 and evaluate using both permutation tests and under transfer-learning scenarios. 

Colex2Lang: Language Embeddings from Semantic Typology

Yiyi Chen, Russa Biswas, Johannes Bjerva

In semantic typology, colexification refers to words with multiple meanings, either related (polysemy) or unrelated (homophony). Studies of cross-linguistic colexification have yielded insights into, e.g., psychology, historical linguistics and cognitive science (Xu et al., 2020; Brochhagen and Boleda, 2022; Schapper and Koptjevskaja-Tamm, 2022). While NLP research up until now has mainly focused on integrating syntactic typology (Naseem et al., 2012; Ponti et al., 2019; Chaudhary et al., 2019; Üstün et al., 2020; Ansell et al., 2021; Oncevay et al., 2022), we here investigate the potential of incorporating semantic typology, of which colexification is an example. We propose a framework for constructing a large-scale synset graph and learning language representations with node embedding algorithms. We demonstrate that cross-lingual colexification patterns provide a distinct signal for modelling language similarity and predicting typological features. Our representations achieve a 9.97% performance gain in predicting lexico-semantic typological features and expectantly contain a weaker syntactic signal. This study is the first attempt to learn language representations and model language similarities using semantic typology at a large scale, setting a new direction for multilingual NLP, especially for low-resource languages. 

Sentiment Classification of Historical Literary in Danish and Norwegian Texts

Ali Allaith, Kirstine Nielsen Degn, Alexander Conroy, Bolette S. Pedersen, Jens Bjerring-Hansen, Daniel Hershcovich

Sentiment classification is valuable for literary analysis, as sentiment is crucial in literary narratives. It can, for example, be used to investigate a hypothesis in the literary analysis of 19th-century Scandinavian novels that the writing of female authors in this period was characterized by negative sentiment, as this paper shows. In order to enable a data-driven analysis of this hypothesis, we create a manually annotated dataset of sentence-level sentiment annotations for novels from this period and use it to train and evaluate various sentiment classification methods. We find that pre-trained multilingual language models outperform models trained on modern Danish, as well as classifiers based on lexical resources. Finally, in classifier-assisted corpus analysis, we confirm the literary hypothesis regarding the author's gender and further shed light on the temporal development of the trend. Our dataset and trained models will be useful for future analysis of historical  Danish and Norwegian literary texts.