Julie Medero


2019

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Reading KITTY: Pitch Range as an Indicator of Reading Skill
Alfredo Gomez | Alicia Ngo | Alessandra Otondo | Julie Medero
Proceedings of the 2019 Workshop on Widening NLP

While affective outcomes are generally positive for the use of eBooks and computer-based reading tutors in teaching children to read, learning outcomes are often poorer (Korat and Shamir, 2004). We describe the first iteration of Reading Kitty, an iOS application that uses NLP and speech processing to focus children’s time on close reading and prosody in oral reading, while maintaining an emphasis on creativity and artifact creation. We also share preliminary results demonstrating that pitch range can be used to automatically predict readers’ skill level.

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Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector
Celena Chen | Celine Park | Jason Dwyer | Julie Medero
Proceedings of the 13th International Workshop on Semantic Evaluation

We use various natural processing and machine learning methods to perform the Hyperpartisan News Detection task. In particular, some of the features we look at are bag-of-words features, the title’s length, number of capitalized words in the title, and the sentiment of the sentences and the title. By adding these features, we see improvements in our evaluation metrics compared to the baseline values. We find that sentiment analysis helps improve our evaluation metrics. We do not see a benefit from feature selection. Overall, our system achieves an accuracy of 0.739, finishing 18th out of 42 submissions to the task. From our work, it is evident that both title features and sentiment of articles are meaningful to the hyperpartisanship of news articles.

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Harvey Mudd College at SemEval-2019 Task 4: The Clint Buchanan Hyperpartisan News Detector
Mehdi Drissi | Pedro Sandoval Segura | Vivaswat Ojha | Julie Medero
Proceedings of the 13th International Workshop on Semantic Evaluation

We investigate the recently developed Bidi- rectional Encoder Representations from Transformers (BERT) model (Devlin et al. 2018) for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from two different BERT models using different proportions of the articles is consistently high, with our best-performing model on the validation set achieving 85% accuracy and the best-performing model on the test set achieving 77%. We further determined that our model exhibits strong consistency, labeling independent slices of the same article identically. Finally, we find that randomizing the order of word pieces dramatically reduces validation accuracy (to approximately 60%), but that shuffling groups of four or more word pieces maintains an accuracy of about 80%, indicating the model mainly gains value from local context.

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Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector
Evan Amason | Jake Palanker | Mary Clare Shen | Julie Medero
Proceedings of the 13th International Workshop on Semantic Evaluation

We use the 600 hand-labelled articles from SemEval Task 4 to hand-tune a classifier with 3000 features for the Hyperpartisan News Detection task. Our final system uses features based on bag-of-words (BoW), analysis of the article title, language complexity, and simple sentiment analysis in a naive Bayes classifier. We trained our final system on the 600,000 articles labelled by publisher. Our final system has an accuracy of 0.653 on the hand-labeled test set. The most effective features are the Automated Readability Index and the presence of certain words in the title. This suggests that hyperpartisan writing uses a distinct writing style, especially in the title.

2016

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HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees
Maury Quijada | Julie Medero
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2013

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Atypical Prosodic Structure as an Indicator of Reading Level and Text Difficulty
Julie Medero | Mari Ostendorf
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2009

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Classifying Factored Genres with Part-of-Speech Histograms
Sergey Feldman | Marius Marin | Julie Medero | Mari Ostendorf
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Annotation Tool Development for Large-Scale Corpus Creation Projects at the Linguistic Data Consortium
Kazuaki Maeda | Haejoong Lee | Shawn Medero | Julie Medero | Robert Parker | Stephanie Strassel
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

The Linguistic Data Consortium (LDC) creates a variety of linguistic resources - data, annotations, tools, standards and best practices - for many sponsored projects. The programming staff at LDC has created the tools and technical infrastructures to support the data creation efforts for these projects, creating tools and technical infrastructures for all aspects of data creation projects: data scouting, data collection, data selection, annotation, search, data tracking and worklow management. This paper introduces a number of samples of LDC programming staff’s work, with particular focus on the recent additions and updates to the suite of software tools developed by LDC. Tools introduced include the GScout Web Data Scouting Tool, LDC Data Selection Toolkit, ACK - Annotation Collection Kit, XTrans Transcription and Speech Annotation Tool, GALE Distillation Toolkit, and the GALE MT Post Editing Workflow Management System.

2006

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An Efficient Approach to Gold-Standard Annotation: Decision Points for Complex Tasks
Julie Medero | Kazuaki Maeda | Stephanie Strassel | Christopher Walker
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

Inter-annotator consistency is a concern for any corpus building effort relying on human annotation. Adjudication is as effective way to locate and correct discrepancies of various kinds. It can also be both difficult and time-consuming. This paper introduces Linguistic Data Consortium (LDC)’s model for decision point-based annotation and adjudication, and describes the annotation tools developed to enable this approach for the Automatic Content Extraction (ACE) Program. Using a customized user interface incorporating decision points, we improved adjudication efficiency over 2004 annotation rates, despite increased annotation task complexity. We examine the factors that lead to more efficient, less demanding adjudication. We further discuss how a decision point model might be applied to annotation tools designed for a wide range of annotation tasks. Finally, we consider issues of annotation tool customization versus development time in the context of a decision point model.

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A New Phase in Annotation Tool Development at the Linguistic Data Consortium: The Evolution of the Annotation Graph Toolkit
Kazuaki Maeda | Haejoong Lee | Julie Medero | Stephanie Strassel
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The Linguistic Data Consortium (LDC) has created various annotated linguistic data for a variety of common task evaluation programs and projects to create shared linguistic resources. The majority of these annotated linguistic data were created with highly customized annotation tools developed at LDC. The Annotation Graph Toolkit (AGTK) has been used as a primary infrastructure for annotation tool development at LDC in recent years. Thanks to the direct feedback from annotation task designers and annotators in-house, annotation tool development at LDC has entered a new, more mature and productive phase. This paper describes recent additions to LDC's annotation tools that are newly developed or significantly improved since our last report at the Fourth International Conference on Language Resource and Evaluation Conference in 2004. These tools are either directly based on AGTK or share a common philosophy with other AGTK tools.