Jing Peng

Also published as: JIng Peng


CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms
Martha Gavidia | Patrick Lee | Anna Feldman | JIng Peng
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Euphemisms have not received much attention in natural language processing, despite being an important element of polite and figurative language. Euphemisms prove to be a difficult topic, not only because they are subject to language change, but also because humans may not agree on what is a euphemism and what is not. Nonetheless, the first step to tackling the issue is to collect and analyze examples of euphemisms. We present a corpus of potentially euphemistic terms (PETs) along with example texts from the GloWbE corpus. Additionally, we present a subcorpus of texts where these PETs are not being used euphemistically, which may be useful for future applications. We also discuss the results of multiple analyses run on the corpus. Firstly, we find that sentiment analysis on the euphemistic texts supports that PETs generally decrease negative and offensive sentiment. Secondly, we observe cases of disagreement in an annotation task, where humans are asked to label PETs as euphemistic or not in a subset of our corpus text examples. We attribute the disagreement to a variety of potential reasons, including if the PET was a commonly accepted term (CAT).

A Report on the Euphemisms Detection Shared Task
Patrick Lee | Anna Feldman | Jing Peng
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)

This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus, and are human-annotated as containing either a euphemistic or literal usage of a PET. In this paper, we present the results and analyze the common themes, methods and findings of the participating teams.

Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms
Patrick Lee | Martha Gavidia | Anna Feldman | Jing Peng
Proceedings of the Second Workshop on Understanding Implicit and Underspecified Language

This paper presents a linguistically driven proof of concept for finding potentially euphemistic terms, or PETs. Acknowledging that PETs tend to be commonly used expressions for a certain range of sensitive topics, we make use of distri- butional similarities to select and filter phrase candidates from a sentence and rank them using a set of simple sentiment-based metrics. We present the results of our approach tested on a corpus of sentences containing euphemisms, demonstrating its efficacy for detecting single and multi-word PETs from a broad range of topics. We also discuss future potential for sentiment-based methods on this task.


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.


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.


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.


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


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)


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