Wiktionary: a new rival for expert-built lexicons? Exploring the possibilities of collaborative lexicography

By Christian M. Meyer, Iryna Gurevych. 2012

126 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Language and Linguistics, Linguistics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jianfeng Gao
2. Zhe Gan
3. Lijuan Wang
4. Chunyuan Li
5. Zicheng Liu
6. Jianwei Yang
7. Lu Yuan
8. Xiaowei Hu

In Language and Linguistics

1. Jianwei Yang
2. Yong Jae Lee
3. Ce Liu
4. Jianfeng Gao
5. Chunyuan Li
6. Haotian Liu
7. Sheng Shen

In Linguistics

1. Jianwei Yang
2. Yong Jae Lee
3. Ce Liu
4. Jianfeng Gao
5. Chunyuan Li
6. Haotian Liu

Citations in the corpus

• ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models (2022, methodology): “Its pre-training setting is the same as UniCL Swin, but external knowledge such as Wiktionary is leveraged in model pre-training.”
• K-LITE: Learning Transferable Visual Models with External Knowledge (2022, methodology): “To this end, we explore a systematic approach to acquire and learn with external knowledge sources from databases such as WordNet [63] and Wik-tionary [62] to train more transferable and sample-efficient visual models.”
• Learning Customized Visual Models with Retrieval-Augmented Knowledge (2023, methodology): “To build transferable visual systems, K-LITE [63] enriches entities in language supervision with structural knowledge in WordNet [49] and Wiktionary [48], in both model training and evaluation stages.”