Kevin Stowe


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Linguistic Analysis Improves Neural Metaphor Detection
Kevin Stowe | Sarah Moeller | Laura Michaelis | Martha Palmer
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

In the field of metaphor detection, deep learning systems are the ubiquitous and achieve strong performance on many tasks. However, due to the complicated procedures for manually identifying metaphors, the datasets available are relatively small and fraught with complications. We show that using syntactic features and lexical resources can automatically provide additional high-quality training data for metaphoric language, and this data can cover gaps and inconsistencies in metaphor annotation, improving state-of-the-art word-level metaphor identification. This novel application of automatically improving training data improves classification across numerous tasks, and reconfirms the necessity of high-quality data for deep learning frameworks.


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Leveraging Syntactic Constructions for Metaphor Identification
Kevin Stowe | Martha Palmer
Proceedings of the Workshop on Figurative Language Processing

Identification of metaphoric language in text is critical for generating effective semantic representations for natural language understanding. Computational approaches to metaphor identification have largely relied on heuristic based models or feature-based machine learning, using hand-crafted lexical resources coupled with basic syntactic information. However, recent work has shown the predictive power of syntactic constructions in determining metaphoric source and target domains (Sullivan 2013). Our work intends to explore syntactic constructions and their relation to metaphoric language. We undertake a corpus-based analysis of predicate-argument constructions and their metaphoric properties, and attempt to effectively represent syntactic constructions as features for metaphor processing, both in identifying source and target domains and in distinguishing metaphoric words from non-metaphoric.

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Improving Classification of Twitter Behavior During Hurricane Events
Kevin Stowe | Jennings Anderson | Martha Palmer | Leysia Palen | Ken Anderson
Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media

A large amount of social media data is generated during natural disasters, and identifying the relevant portions of this data is critical for researchers attempting to understand human behavior, the effects of information sources, and preparatory actions undertaken during these events. In order to classify human behavior during hazard events, we employ machine learning for two tasks: identifying hurricane related tweets and classifying user evacuation behavior during hurricanes. We show that feature-based and deep learning methods provide different benefits for tweet classification, and ensemble-based methods using linguistic, temporal, and geospatial features can effectively classify user behavior.

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Developing and Evaluating Annotation Procedures for Twitter Data during Hazard Events
Kevin Stowe | Martha Palmer | Jennings Anderson | Marina Kogan | Leysia Palen | Kenneth M. Anderson | Rebecca Morss | Julie Demuth | Heather Lazrus
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

When a hazard such as a hurricane threatens, people are forced to make a wide variety of decisions, and the information they receive and produce can influence their own and others’ actions. As social media grows more popular, an increasing number of people are using social media platforms to obtain and share information about approaching threats and discuss their interpretations of the threat and their protective decisions. This work aims to improve understanding of natural disasters through social media and provide an annotation scheme to identify themes in user’s social media behavior and facilitate efforts in supervised machine learning. To that end, this work has three contributions: (1) the creation of an annotation scheme to consistently identify hazard-related themes in Twitter, (2) an overview of agreement rates and difficulties in identifying annotation categories, and (3) a public release of both the dataset and guidelines developed from this scheme.


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Identifying and Categorizing Disaster-Related Tweets
Kevin Stowe | Michael J. Paul | Martha Palmer | Leysia Palen | Kenneth Anderson
Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media


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Renewing and Revising SemLink
Claire Bonial | Kevin Stowe | Martha Palmer
Proceedings of the 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data