A survey of types of text noise and techniques to handle noisy text
By L. Venkata Subramaniam, Shourya Roy, Tanveer A. Faruquie, Sumit Negi. 2009
105 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
- Douwe Kiela
- Mohit Bansal
- José A. R. Fonollosa
- Nazneen Rajani
- Tristan Thrush
- Yonatan Belinkov
- Yacine Jernite
- Yejin Choi
In Computer Vision and Pattern Recognition
- Douwe Kiela
- Nazneen Rajani
- Tristan Thrush
- José A. R. Fonollosa
- Angelina McMillan-Major
- Lluís Formiga
- Yacine Jernite
- Alexandra Sasha Luccioni
In Natural Language Processing
- José A. R. Fonollosa
- Tristan Thrush
- Douwe Kiela
- Nazneen Rajani
- Angelina McMillan-Major
- Lluís Formiga
- Yacine Jernite
- Alexandra Sasha Luccioni
Citations in the corpus
- Measuring Data (2022, background): “They have been defined for text as well (cf. (Subramaniam et al., 2009)), although they tend to require additional models of language.”
- Dealing with Input Noise in Statistical Machine Translation (2012, methodology): “The text of chats, comments, tweets or SMS’s is usually full of misspelled words, slang or wrong abbreviations introducing noise into the text data (Subramaniam et al., 2009; Yvon, 2010) and affecting NLP tasks such as text-mining, machine translation or opinion classification (Dey and Haque, 2009).”