Lee Becker

Also published as: Lee A. Becker


Multiple Instance Learning for Content Feedback Localization without Annotation
Scott Hellman | William Murray | Adam Wiemerslage | Mark Rosenstein | Peter Foltz | Lee Becker | Marcia Derr
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

Automated Essay Scoring (AES) can be used to automatically generate holistic scores with reliability comparable to human scoring. In addition, AES systems can provide formative feedback to learners, typically at the essay level. In contrast, we are interested in providing feedback specialized to the content of the essay, and specifically for the content areas required by the rubric. A key objective is that the feedback should be localized alongside the relevant essay text. An important step in this process is determining where in the essay the rubric designated points and topics are discussed. A natural approach to this task is to train a classifier using manually annotated data; however, collecting such data is extremely resource intensive. Instead, we propose a method to predict these annotation spans without requiring any labeled annotation data. Our approach is to consider AES as a Multiple Instance Learning (MIL) task. We show that such models can both predict content scores and localize content by leveraging their sentence-level score predictions. This capability arises despite never having access to annotation training data. Implications are discussed for improving formative feedback and explainable AES models.


ClearTK 2.0: Design Patterns for Machine Learning in UIMA
Steven Bethard | Philip Ogren | Lee Becker
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.


AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion
Lee Becker | George Erhart | David Skiba | Valentine Matula
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)


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Question Ranking and Selection in Tutorial Dialogues
Lee Becker | Martha Palmer | Sarel van Vuuren | Wayne Ward
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

Mind the Gap: Learning to Choose Gaps for Question Generation
Lee Becker | Sumit Basu | Lucy Vanderwende
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


DISCUSS: A dialogue move taxonomy layered over semantic representations
Lee Becker | Wayne Ward | Sarel van Vuuren | Martha Palmer
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)


PHONY: A Heuristic Phonological Analyzer
Lee A. Becker
19th Annual Meeting of the Association for Computational Linguistics