Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks
Existing Natural Language Processing (NLP) resources often lack the task-specific information required for real-world problems and provide limited coverage of lesser-known or newly introduced entities. For example, business organizations and health care providers may need to be classified into a variety of different taxonomic schemes for specific application tasks. Our goal is to enable domain experts to easily create a task-specific classifier for entities by providing only entity names and gold labels as training data. Our framework then dynamically acquires descriptive text about each entity, which is subsequently used as the basis for producing a text-based classifier. We propose a no...
| Authors | Fahmida Alam, Ellen Riloff |
|---|---|
| Primary category | cs.CL |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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