| 1. |
- Danilova, Vera, et al.
(författare)
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UD-MULTIGENRE : a UD-Based Dataset Enriched with Instance-Level Genre Annotations
- 2023
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Ingår i: Proceedings of the 3rd Workshop on Multi-lingual Representation Learning (MRL). - : Association for Computational Linguistics. - 9798891760561 ; , s. 253-267
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Konferensbidrag (refereegranskat)abstract
- Prior research on the impact of genre on cross-lingual dependency parsing has suggested that genre is an important signal. However, these studies suffer from a scarcity of reliable data for multiple genres and languages. While Universal Dependencies (UD), the only available large-scale resource for cross-lingual dependency parsing, contains data from diverse genres, the documentation of genre labels is missing, and there are multiple inconsistencies. This makes studies of the impact of genres difficult to design. To address this, we present a new dataset, UD-MULTIGENRE, where 17 genres are defined and instance-level annotations of these are applied to a subset of UD data, covering 38 languages. It provides a rich ground for research related to text genre from a multilingual perspective. Utilizing this dataset, we can overcome the data shortage that hindered previous research and reproduce experiments from earlier studies with an improved setup. We revisit a previous study that used genre-based clusters and show that the clusters for most target genres provide a mix of genres. We compare training data selection based on clustering and gold genre labels and provide an analysis of the results. The dataset is publicly available. (https://github.com/UppsalaNLP/UD-MULTIGENRE)
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| 2. |
- Skeppstedt, Maria, Dr. 1977-, et al.
(författare)
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Topics in Periodicals from the Swedish Diabetes Association 1949 – 1990: Extending the Topic Modelling Tool Topics2Themes with a Timeline Visualisation
- 2024
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Ingår i: Selected papers from the CLARIN Annual Conference 2023.
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Konferensbidrag (refereegranskat)abstract
- Existing methods for visualising temporal topic models typically present the information in an aggregated form, and do not offer any possibility to track the specific texts responsible for the change in topic prevalence over time. We present a new type of topic modelling-based timeline visualisation. It still provides an overview with aggregated topic information suitable for distant reading, while also allowing the user to gradually zoom into the image for more detail. At the most detailed level, the individual texts can be reached, which makes it possible to switch to close reading. The timeline visualisation was implemented as an extension of the topic modelling tool Topics2Themes, but this visualisation technique can be adapted to other topic modelling tools and algorithms. We showcase the timeline visualisation on a corpus of periodicals from the Swedish Diabetes Association, which is one of the patient organisation corpora studied within the interdisciplinary project ActDisease. One timeline visualisation was generated for the entire corpus. Additionally, we generated a timeline focusing on the texts that contain the word “dietitian”. The two timelines, including the functionality to zoom into the graphs and reach the texts, were used to analyse the topics and how they vary. It could be concluded that some of the topics and topic timelines were predictable, while others revealed content that might be less expected. These results indicate validity of the method applied, and they also show that this visualisation technique could help us learn something new.
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