SENSEVAL: an exercise in evaluating world sense disambiguation programs

By Adam Kilgarriff. 1998

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

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

In Natural Language Processing

1. Emily L. Denton
2. Inioluwa Deborah Raji
3. A. Hanna
4. Emily M. Bender
5. Amandalynne Paullada
6. Spandana Gella
7. Frank Keller
8. Axel-Cyrille Ngonga Ngomo

In Evaluation & Benchmarks

1. Emily L. Denton
2. A. Hanna
3. Inioluwa Deborah Raji
4. Frank Keller
5. Axel-Cyrille Ngonga Ngomo
6. Mirella Lapata
7. Ricardo Usbeck
8. Emily M. Bender

Citations in the corpus

• AI and the Everything in the Whole Wide World Benchmark (2021, background): “, 2009] and word sense disambiguation (SENSEVAL)[Kilgarri, 1998].”
• GERBIL - Benchmarking Named Entity Recognition and Linking consistently (2017, background): “The Senseval challenge, originally concerned with classical NLP disciplines, has widened its focus in 2007 and changed its name to SemEval to account for the recently recognized impact of semantic technologies [29].”
• Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings (2016, background): “tion Word sense disambiguation (WSD) is a widely studied task in natural language processing: given a word and its context, assign the correct sense of the word based on a pre-defined sense inventory (Kilgarrif, 1998). WSD is useful for a range of NLP tasks, including information retrieval, informati”