Papers by Miriam Butt
Dependency Parsing for Urdu: Resources, Conversions and Learning (2020.lrec-1)
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| Challenge: | Existing treebanks for Urdu are under-resourced due to lack of resources. |
| Approach: | They propose to convert existing treebanks into a common format that is based on Universal Dependencies. |
| Outcome: | The proposed format outperforms the MaltParser and a transition-based BiLSTM parser with word embeddings and significantly improves parsing accuracy. |
lingvis.io - A Linguistic Visual Analytics Framework (P19-3)
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Mennatallah El-Assady, Wolfgang Jentner, Fabian Sperrle, Rita Sevastjanova, Annette Hautli-Janisz, Miriam Butt, Daniel Keim
| Challenge: | Using a modular framework, linguistic visual analytics applications can be rapidly prototypized using a web-based framework. |
| Approach: | They propose a modular framework for rapid prototyping of linguistic, web-based, visual analytics applications. |
| Outcome: | The proposed framework supports rapid prototyping of linguistic, web-based, visual analytics applications. |
GRIT: A Dataset of Group Reference Recognition in Italian (2024.lrec-main)
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| Challenge: | a task of automatically recognizing group references has not yet gained much attention within NLP. |
| Approach: | They propose a large-scale dataset for automatic group reference recognition in italian . they verify the validity of the task using a fine-tuned BERT model . |
| Outcome: | The proposed dataset proves that it can be applied to political text analysis and social media analysis. |
A Multilingual Approach to Question Classification (L18-1)
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| Challenge: | Existing work on questions has focused on understanding the structure of questions per se . a few approaches explicitly focus on information-seeking questions, but this work is either based on big data or crowdsourcing. |
| Approach: | They propose a dependency-parsed, parallel multilingual corpus of information-seeking and non-information-seeing questions . they employ a linguistically motivated rule-based system that uses linguistic cues from one language to help classify questions across other languages. |
| Outcome: | The proposed system correctly classifies questions in 79% of cases, compared to other systems. |