Fine-grained entity disambiguation through numeric pattern awareness in transformer models
Abstract Knowledge base question answering systems rely on entity linking to connect textual mentions in natural language with corresponding entities in a structured knowledge base. While conventional methods perform well in static or generic scenarios, they often struggle in dynamic contexts that r...
Saved in:
| Main Authors: | Jaeeun Jang, Sangmin Kim, Howon Moon, Sang Heon Shin, Mira Yun, Charles Wiseman |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
|
| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01936-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Fine-Grained Tasks for Crowdsourced Entity Resolution
by: Tiezheng Nie, et al.
Published: (2024-12-01) -
Sentences, entities, and keyphrases extraction from consumer health forums using multi-task learning
by: Tsaqif Naufal, et al.
Published: (2025-05-01) -
A Method of Word Sense Disambiguation with Restricted Boltzmann Machine
by: ZHANG Chun-xiang, et al.
Published: (2019-10-01) -
A Method of Word Sense Disambiguation with Recurrent Netural Networks
by: ZHANG Chunxiang, et al.
Published: (2020-02-01) -
Chinese Word Sense Disambiguation Based on Word translation and Part of speech
by: ZHANG Chunxiang, et al.
Published: (2020-06-01)