Jing Peng


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Neural Network Prediction of Censorable Language
Kei Yin Ng | Anna Feldman | Jing Peng | Chris Leberknight
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

Internet censorship imposes restrictions on what information can be publicized or viewed on the Internet. According to Freedom House’s annual Freedom on the Net report, more than half the world’s Internet users now live in a place where the Internet is censored or restricted. China has built the world’s most extensive and sophisticated online censorship system. In this paper, we describe a new corpus of censored and uncensored social media tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts that mention ‘sensitive’ topics or authored by ‘sensitive’ users. We use this corpus to build a neural network classifier to predict censorship. Our model performs with a 88.50% accuracy using only linguistic features. We discuss these features in detail and hypothesize that they could potentially be used for censorship circumvention.


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Designing a Russian Idiom-Annotated Corpus
Katsiaryna Aharodnik | Anna Feldman | Jing Peng
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Linguistic Characteristics of Censorable Language on SinaWeibo
Kei Yin Ng | Anna Feldman | Jing Peng | Chris Leberknight
Proceedings of the First Workshop on Natural Language Processing for Internet Freedom

This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability.


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Experiments in Idiom Recognition
Jing Peng | Anna Feldman
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Some expressions can be ambiguous between idiomatic and literal interpretations depending on the context they occur in, e.g., ‘sales hit the roof’ vs. ‘hit the roof of the car’. We present a novel method of classifying whether a given instance is literal or idiomatic, focusing on verb-noun constructions. We report state-of-the-art results on this task using an approach based on the hypothesis that the distributions of the contexts of the idiomatic phrases will be different from the contexts of the literal usages. We measure contexts by using projections of the words into vector space. For comparison, we implement Fazly et al. (2009)’s, Sporleder and Li (2009)’s, and Li and Sporleder (2010b)’s methods and apply them to our data. We provide experimental results validating the proposed techniques.


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Classifying Idiomatic and Literal Expressions Using Vector Space Representations
Jing Peng | Anna Feldman | Hamza Jazmati
Proceedings of the International Conference Recent Advances in Natural Language Processing


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Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Jing Peng | Anna Feldman | Ekaterina Vylomova
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)


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Detecting the Countability of English Compound Nouns Using Web-based Models
Jing Peng | Kenji Araki
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts