Modeling and solving term mismatch for full-text retrieval
By Le Zhao. 2012
13 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Information Retrieval & Search
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
- Omar Khattab
- Matei A. Zaharia
- Torsten Suel
- Nicola Tonellotto
- Antonio Mallia
- Christopher Potts
- Jimmy J. Lin
- Sebastian Hofstätter
In Computer Vision and Pattern Recognition
- Omar Khattab
- Matei A. Zaharia
- Nicola Tonellotto
- Christopher Potts
- Keshav Santhanam
- Gautier Izacard
- Edouard Grave
- Martin Jaggi
In Information Systems
- Omar Khattab
- Matei A. Zaharia
- Torsten Suel
- Nicola Tonellotto
- Antonio Mallia
- Jimmy J. Lin
- Sheng-Chieh Lin
- Jheng-Hong Yang
In Information Retrieval & Search
- Omar Khattab
- Matei A. Zaharia
- Torsten Suel
- Nicola Tonellotto
- Antonio Mallia
- Christopher Potts
- Jimmy J. Lin
- Sheng-Chieh Lin
Citations in the corpus
- ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (2020, background): “By computing deeply-contextualized semantic representations of query–document pairs, these LMs help bridge the pervasive vocabulary mismatch [22, 42] between documents and queries [30].”
- Learning Passage Impacts for Inverted Indexes (2021, background): “DeepImpact improves impact-scoremodeling and tackles the vocabulary-mismatch problem [25] between queries and documents.”