Search Results for author: Steven Wilson

Found 16 papers, 2 papers with code

Analyzing the Effects of Annotator Gender across NLP Tasks

1 code implementation NLPerspectives (LREC) 2022 Laura Biester, Vanita Sharma, Ashkan Kazemi, Naihao Deng, Steven Wilson, Rada Mihalcea

Recent studies have shown that for subjective annotation tasks, the demographics, lived experiences, and identity of annotators can have a large impact on how items are labeled.

Natural Language Inference

Narrative Detection and Feature Analysis in Online Health Communities

no code implementations NAACL (WNU) 2022 Achyutarama Ganti, Steven Wilson, Zexin Ma, Xinyan Zhao, Rong Ma

Narratives have been shown to be an effective way to communicate health risks and promote health behavior change, and given the growing amount of health information being shared on social media, it is crucial to study health-related narratives in social media.

text-classification Text Classification

Should a Chatbot be Sarcastic? Understanding User Preferences Towards Sarcasm Generation

no code implementations ACL 2022 Silviu Vlad Oprea, Steven Wilson, Walid Magdy

Previous sarcasm generation research has focused on how to generate text that people perceive as sarcastic to create more human-like interactions.


Emoji and Self-Identity in Twitter Bios

no code implementations EMNLP (NLP+CSS) 2020 Jinhang Li, Giorgos Longinos, Steven Wilson, Walid Magdy

Emoji are widely used to express emotions and concepts on social media, and prior work has shown that users’ choice of emoji reflects the way that they wish to present themselves to the world.

Diachronic Embeddings for People in the News

no code implementations EMNLP (NLP+CSS) 2020 Felix Hennig, Steven Wilson

Previous English-language diachronic change models based on word embeddings have typically used single tokens to represent entities, including names of people.

named-entity-recognition Named Entity Recognition +2

Chandler: An Explainable Sarcastic Response Generator

no code implementations EMNLP (ACL) 2021 Silviu Oprea, Steven Wilson, Walid Magdy

We introduce Chandler, a system that generates sarcastic responses to a given utterance.

Embedding Structured Dictionary Entries

no code implementations EMNLP (insights) 2020 Steven Wilson, Walid Magdy, Barbara McGillivray, Gareth Tyson

Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space.

Learning Word Embeddings Multi-Task Learning

SemEval 2021 Task 7: HaHackathon, Detecting and Rating Humor and Offense

no code implementations SEMEVAL 2021 J. A. Meaney, Steven Wilson, Luis Chiruzzo, Adam Lopez, Walid Magdy

Our subtasks were binary humor detection, prediction of humor and offense ratings, and a novel controversy task: to predict if the variance in the humor ratings was higher than a specific threshold.

Humor Detection

Smash at SemEval-2020 Task 7: Optimizing the Hyperparameters of ERNIE 2.0 for Humor Ranking and Rating

no code implementations SEMEVAL 2020 J. A. Meaney, Steven Wilson, Walid Magdy

The use of pre-trained language models such as BERT and ULMFiT has become increasingly popular in shared tasks, due to their powerful language modelling capabilities.

Classification Language Modelling +1

Small Town or Metropolis? Analyzing the Relationship between Population Size and Language

no code implementations LREC 2020 Amy Rechkemmer, Steven Wilson, Rada Mihalcea

Using a set of over 2 million posts from distinct Twitter users around the country dating back as far as 2014, we ask the following question: is there a difference in how Americans express themselves online depending on whether they reside in an urban or rural area?

Urban Dictionary Embeddings for Slang NLP Applications

no code implementations LREC 2020 Steven Wilson, Walid Magdy, Barbara McGillivray, Kiran Garimella, Gareth Tyson

The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models.

Sarcasm Detection Semantic Similarity +3

Measuring Semantic Relations between Human Activities

no code implementations IJCNLP 2017 Steven Wilson, Rada Mihalcea

The things people do in their daily lives can provide valuable insights into their personality, values, and interests.

Semantic Textual Similarity

Stateology: State-Level Interactive Charting of Language, Feelings, and Values

no code implementations20 Dec 2016 Konstantinos Pappas, Steven Wilson, Rada Mihalcea

People's personality and motivations are manifest in their everyday language usage.

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