A Conversational Movie Search System Based on Conditional Random Fields
By Jingjing Liu, D. Scott Cyphers, Panupong Pasupat, Ian McGraw, James R. Glass. 2012
52 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
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
- Jianfeng Gao
- Hung-yi Lee
- Eric Wallace
- Luke S. Zettlemoyer
- Sameer Singh
- Li Deng
- Hannaneh Hajishirzi
- Dong Yu
In Computer Vision and Pattern Recognition
- Kaisheng Yao
- Eric Wallace
- Geoffry Zweig
- Sameer Singh
- Dong Yu
- Hung-yi Lee
- Mei-Yuh Hwang
- Tsung-Hsien Wen
In Natural Language Processing
Citations in the corpus
- Calibrate Before Use: Improving Few-Shot Performance of Language Models (2021, methodology): “Information Extraction We consider information extraction using two slot filling datasets, ATIS (Hemphill et al., 1990) and MIT Movies trivia10k13 (Liu et al., 2012).”
- Recurrent neural networks for language understanding (2013, background): “In classical LU systems [17, 18, 19, 20, 21, 22, 23, 24, 25], one of the key tasks is to label words with semantic meaning.”
- Recurrent neural network based language model personalization by social network crowdsourcing (2013, methodology): “The MIT movie browser [33, 34] relied on Amazon Mechanical Turk, the most famous crowdsourcing platform, to build a crowd-supervised spoken language system.”