Open AccessProceedings Article
What to do about bad language on the internet
Jacob Eisenstein
- pp 359-369
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TLDR
A critical review of the NLP community's response to the landscape of bad language is offered, and a quantitative analysis of the lexical diversity of social media text, and its relationship to other corpora is presented.Abstract:
The rise of social media has brought computational linguistics in ever-closer contact with bad language: text that defies our expectations about vocabulary, spelling, and syntax. This paper surveys the landscape of bad language, and offers a critical review of the NLP community’s response, which has largely followed two paths: normalization and domain adaptation. Each approach is evaluated in the context of theoretical and empirical work on computer-mediated communication. In addition, the paper presents a quantitative analysis of the lexical diversity of social media text, and its relationship to other corpora.read more
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Book
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TL;DR: This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
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Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling
TL;DR: By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference.