Diane Napolitano


Writing Mentor: Self-Regulated Writing Feedback for Struggling Writers
Nitin Madnani | Jill Burstein | Norbert Elliot | Beata Beigman Klebanov | Diane Napolitano | Slava Andreyev | Maxwell Schwartz
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

Writing Mentor is a free Google Docs add-on designed to provide feedback to struggling writers and help them improve their writing in a self-paced and self-regulated fashion. Writing Mentor uses natural language processing (NLP) methods and resources to generate feedback in terms of features that research into post-secondary struggling writers has classified as developmental (Burstein et al., 2016b). These features span many writing sub-constructs (use of sources, claims, and evidence; topic development; coherence; and knowledge of English conventions). Prelimi- nary analysis indicates that users have a largely positive impression of Writing Mentor in terms of usability and potential impact on their writing.


A Report on the 2017 Native Language Identification Shared Task
Shervin Malmasi | Keelan Evanini | Aoife Cahill | Joel Tetreault | Robert Pugh | Christopher Hamill | Diane Napolitano | Yao Qian
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Native Language Identification (NLI) is the task of automatically identifying the native language (L1) of an individual based on their language production in a learned language. It is typically framed as a classification task where the set of L1s is known a priori. Two previous shared tasks on NLI have been organized where the aim was to identify the L1 of learners of English based on essays (2013) and spoken responses (2016) they provided during a standardized assessment of academic English proficiency. The 2017 shared task combines the inputs from the two prior tasks for the first time. There are three tracks: NLI on the essay only, NLI on the spoken response only (based on a transcription of the response and i-vector acoustic features), and NLI using both responses. We believe this makes for a more interesting shared task while building on the methods and results from the previous two shared tasks. In this paper, we report the results of the shared task. A total of 19 teams competed across the three different sub-tasks. The fusion track showed that combining the written and spoken responses provides a large boost in prediction accuracy. Multiple classifier systems (e.g. ensembles and meta-classifiers) were the most effective in all tasks, with most based on traditional classifiers (e.g. SVMs) with lexical/syntactic features.


Spoken Text Difficulty Estimation Using Linguistic Features
Su-Youn Yoon | Yeonsuk Cho | Diane Napolitano
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications


Online Readability and Text Complexity Analysis with TextEvaluator
Diane Napolitano | Kathleen Sheehan | Robert Mundkowsky
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations


A Two-Stage Approach for Generating Unbiased Estimates of Text Complexity
Kathleen M. Sheehan | Michael Flor | Diane Napolitano
Proceedings of the Workshop on Natural Language Processing for Improving Textual Accessibility

Robust Systems for Preposition Error Correction Using Wikipedia Revisions
Aoife Cahill | Nitin Madnani | Joel Tetreault | Diane Napolitano
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies