Search Results for author: Cecilia Ovesdotter Alm

Found 22 papers, 0 papers with code

Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets

no code implementations LREC 2022 Farhad Akhbardeh, Marcos Zampieri, Cecilia Ovesdotter Alm, Travis Desell

Event identification in technical logbooks poses challenges given the limited logbook data available in specific technical domains, the large set of possible classes, and logbook entries typically being in short form and non-standard technical language.

Domain Adaptation Transfer Learning

Handling Extreme Class Imbalance in Technical Logbook Datasets

no code implementations ACL 2021 Farhad Akhbardeh, Cecilia Ovesdotter Alm, Marcos Zampieri, Travis Desell

In this paper we focus on the problem of technical issue classification by considering logbook datasets from the automotive, aviation, and facilities maintenance domains.

A dataset for identifying actionable feedback in collaborative software development

no code implementations ACL 2018 Benjamin S. Meyers, Nuthan Munaiah, Emily Prud{'}hommeaux, Andrew Meneely, Josephine Wolff, Cecilia Ovesdotter Alm, Pradeep Murukannaiah

Software developers and testers have long struggled with how to elicit proactive responses from their coworkers when reviewing code for security vulnerabilities and errors.

Sensing and Learning Human Annotators Engaged in Narrative Sensemaking

no code implementations NAACL 2018 McKenna Tornblad, Luke Lapresi, Christopher Homan, Raymond Ptucha, Cecilia Ovesdotter Alm

While labor issues and quality assurance in crowdwork are increasingly studied, how annotators make sense of texts and how they are personally impacted by doing so are not.

An Analysis and Visualization Tool for Case Study Learning of Linguistic Concepts

no code implementations EMNLP 2017 Cecilia Ovesdotter Alm, Benjamin Meyers, Emily Prud{'}hommeaux

We present an educational tool that integrates computational linguistics resources for use in non-technical undergraduate language science courses.

Active Learning

Analyzing Gender Bias in Student Evaluations

no code implementations COLING 2016 Andamlak Terkik, Emily Prud{'}hommeaux, Cecilia Ovesdotter Alm, Christopher Homan, Scott Franklin

University students in the United States are routinely asked to provide feedback on the quality of the instruction they have received.

Sentiment Analysis

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