Serina Chang


2019

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Automatically Inferring Gender Associations from Language
Serina Chang | Kathy McKeown
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings – one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.

2018

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Detecting Gang-Involved Escalation on Social Media Using Context
Serina Chang | Ruiqi Zhong | Ethan Adams | Fei-Tzin Lee | Siddharth Varia | Desmond Patton | William Frey | Chris Kedzie | Kathy McKeown
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.

2017

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Crowd-Sourced Iterative Annotation for Narrative Summarization Corpora
Jessica Ouyang | Serina Chang | Kathy McKeown
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We present an iterative annotation process for producing aligned, parallel corpora of abstractive and extractive summaries for narrative. Our approach uses a combination of trained annotators and crowd-sourcing, allowing us to elicit human-generated summaries and alignments quickly and at low cost. We use crowd-sourcing to annotate aligned phrases with the text-to-text generation techniques needed to transform each phrase into the other. We apply this process to a corpus of 476 personal narratives, which we make available on the Web.