Papers by Artuur Leeuwenberg
A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages (C18-2)
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| Challenge: | DAMESRL is an open source framework for deep semantic role labeling . language-specific characteristics and the available amount of training data influence the optimal model structure . |
| Approach: | They propose an open-source framework for deep semantic role labeling that is available under the Apache 2.0 license. |
| Outcome: | The proposed framework is available under the Apache 2.0 license. |
Word-Level Loss Extensions for Neural Temporal Relation Classification (C18-1)
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| Challenge: | Unsupervised pre-trained word embeddings are used for many tasks in natural language processing to leverage unlabeled textual data. |
| Approach: | They extend the model's task loss with an unsupervised auxiliary loss on the word-embedding level of the model to ensure that the learned word representations contain both task-specific features and more general features. |
| Outcome: | The proposed model improves on the task of extracting narrative containment relations from clinical records using a general-domain part-of-speech tagger as linguistic resource. |
Temporal Information Extraction by Predicting Relative Time-lines (D18-1)
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| Challenge: | a new paradigm for temporal information extraction from text evades the relation extraction phase because there are n 2 possible entity pairs in a text with n temporal entities. |
| Approach: | They propose a method to construct a linear time-line from a set of temporal relations from text without the intermediate step of prediction of tempor relations. |
| Outcome: | The proposed method predicts start and end-points without intermediate step of prediction of temporal relations . it evades phase 2 because there are n 2 possible entity pairs in the extraction phase . |