Open AccessProceedings Article
What to do about bad language on the internet
Jacob Eisenstein
- pp 359-369
Reads0
Chats0
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
Citations
More filters
DissertationDOI
Frisian on social media: the vitality of minority languages in a multilingual online world
Posted Content
Improving Formality Style Transfer with Context-Aware Rule Injection.
Zonghai Yao,Hong Yu +1 more
TL;DR: The authors proposed Context-Aware Rule Injection (CARI), an innovative method for formality style transfer (FST) which injects multiple rules into an end-to-end BERT-based encoder and decoder model.
Proceedings Article
Multi-way Variational NMT for UGC: Improving Robustness in Zero-shot Scenarios via Mixture Density Networks
TL;DR: The authors presented a novel Variational Neural Machine Translation (VNMT) architecture with enhanced robustness properties, which investigated through a detailed case-study addressing noisy French user-generated content (UGC) translation to English.
Journal ArticleDOI
Corpus Expansion for Neural CWS on Microblog-Oriented Data with λ-Active Learning Approach
TL;DR: Experiments on the benchmark datasets of NLPCC 2015 show that the λ-active learning method outperforms the baseline system and the state-of-the-art method, and the performances of the DNNs trained on the extended corpus are significantly improved.
Book ChapterDOI
Fine-Grained POS Tagging of German Social Media and Web Texts
TL;DR: This paper takes a simple Hidden Markov Model based tagger as a starting point, and extends it with a distributional approach to estimating lexical (emission) probabilities of out-of-vocabulary words, which occur frequently in social media and web texts and are a major reason for the low performance of off-the-shelf taggers on these types of text.
References
More filters
Proceedings ArticleDOI
Earthquake shakes Twitter users: real-time event detection by social sensors
TL;DR: This paper investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event and produces a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
Journal ArticleDOI
Critical questions for big data
danah boyd,Kate Crawford +1 more
TL;DR: The era of Big Data has begun as discussed by the authors, where diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people.
Proceedings ArticleDOI
Feature-rich part-of-speech tagging with a cyclic dependency network
TL;DR: A new part-of-speech tagger is presented that demonstrates the following ideas: explicit use of both preceding and following tag contexts via a dependency network representation, broad use of lexical features, and effective use of priors in conditional loglinear models.
Book
Natural Language Processing with Python
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.
Proceedings ArticleDOI
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.