Papers by Toms Bergmanis
Open Terminology Management and Sharing Toolkit for Federation of Terminology Databases (2022.lrec-1)
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| Challenge: | Terminology is also needed in AI applications such as machine translation, speech recognition, information extraction, and other natural language processing tools. |
| Approach: | They propose a terminology management solution that facilitates standards-based sharing and management of terminology resources by providing the EuroTermBank Toolkit. |
| Outcome: | The EuroTermBank Toolkit facilitates standards-based sharing and management of terminology resources by participating in federated databases. |
Context Sensitive Neural Lemmatization with Lematus (N18-1)
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| Challenge: | a new contextsensitive lemmatizer is designed to improve performance on unseen and ambiguous words. |
| Approach: | They propose a context-sensitive lemmatizer which incorporates character-level sentence context. |
| Outcome: | The proposed model outperforms the best competitor in a data setting and lower-resource setting . the model includes context for ambiguous words, but the latter has a greater effect on performance . |
Facilitating Terminology Translation with Target Lemma Annotations (2021.eacl-main)
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| Challenge: | Recent work on terminology integration assumes that the correct morphological forms are apriori known. |
| Approach: | They propose to train machine translation systems using a source-side data augmentation method that annotates randomly selected source language words with their target language lemmas. |
| Outcome: | The proposed method improves terminology translation accuracy in Latvian and Baltic languages. |
Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text (N19-1)
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| Challenge: | Using context-sensitive approaches to lemmatization can improve accuracy on unseen and unseense words. |
| Approach: | They propose to use inflection tables and Wikipedia sentences to train a lemmatizer with little or no labeled corpus data to combine type-based learning with context. |
| Outcome: | The proposed model generalizes from unambiguous examples, improving overall and especially on unseen words. |