PhD student in the Department of Computer and Information Science at the University of Pennsylvania
Member of the Natural Language Processing group
Office: Levine 514
Email: epitler at seas dot upenn dot edu
Advisor: Mitch Marcus
Emily Pitler, Sampath Kannan, and Mitchell Marcus. Dynamic Programming for Higher Order Parsing of Gap-Minding Trees. (To appear) EMNLP, 2012.
Emily Pitler. Attacking Parsing Bottlenecks with Unlabeled Data and Relevant Factorizations. (To appear) ACL, 2012.
Ani Nenkova, Jieun Chae, Annie Louis, and Emily Pitler.
Structural Features for Predicting the Linguistic Quality of Text:
Applications to Machine Translation, Automatic Summarization and
Human-Authored Text. In Emiel Krahmer and Mariet Theune, editors,
Empirical Methods in Natural Language Generation: Data-oriented
Methods and Empirical Evaluation, Springer, 2010.
Emily Pitler, Shane Bergsma, Dekang Lin, and Kenneth Church.
Using Web-scale N-grams to Improve Base NP Parsing Performance. Proceedings of COLING, 2010.
Emily Pitler, Annie Louis, and Ani Nenkova.
Automatic Evaluation of Linguistic Quality in Multi-Document Summarization.
Proceedings of ACL, 2010.
Shane Bergsma, Emily Pitler, and Dekang Lin.
Creating Robust Supervised Classifiers via Web-Scale N-gram Data.
Proceedings of ACL, 2010.
Dekang Lin, Kenneth Church, Heng Ji, Satoshi Sekine, David
Yarowsky, Shane Bergsma, Kailash Patil, Emily Pitler, Rachel Lathbury,
Vikram Rao, Kapil Dalwani and Sushant Narsale. New Tools for Web-Scale N-grams. Proceedings of LREC, 2010.
Emily Pitler, Annie Louis, and Ani Nenkova. Automatic Sense Prediction for Implicit Discourse Relations in Text. Proceedings of ACL, 2009. Slides
Emily Pitler and Ani Nenkova. Using Syntax to Disambiguate Explicit Discourse Connectives in Text. Proceedings of ACL, short paper, 2009.
A tool to identify discourse connectives and their sense is available here: Discourse Connective Tool
Emily Pitler and Ken Church. Using Word-Sense Disambiguation Methods to Classify Web Queries by Intent. Proceedings of EMNLP, 2009.
Emily Pitler and Ani Nenkova. Revisiting Readability: A Unified Framework for Predicting Text Quality. Proceedings of EMNLP, 2008.
Emily Pitler, Mridhula Raghupathy, Hena Mehta, Ani Nenkova, Alan Lee, Aravind Joshi. Easily Identifiable Discourse Relations. Proceedings of COLING, 2008. Poster paper.
The extended version is Easily Identifiable Discourse Relations. University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-08-24.
Dongyu Lin, Emily Pitler, Dean P. Foster, Lyle H. Ungar. In Defense of L0. Sparse Optimization and Variable Selection, Workshop, ICML/COLT/UAI, July, 2008.
Samarth Keshava and Emily Pitler. A Simpler, Intuitive Approach to Morpheme Induction. 2nd Pascal Challenges Workshop, Venice, Italy, 2006. The code is available here: reports.pl.
Student co-chair of the Student Research Workshop at ACL 2011.
Emily Pitler. Methods for Sentence Compression. University of Pennsylvania Technical Report MS-CIS-10-20. In fulfillment of the Department of Computer and Information Science Written Preliminary Exam II.
2009: MSE in Computer and Information Science, University of Pennsylvania
2007: BS in Computer Science, Yale University
Summer 2010: Intern, Google Research
Summer 2009: Unsupervised Learning of Lexical Knowledge from N-grams, Johns Hopkins Center for Language and Speech Processing Summer Workshop
Summer 2008: Research Intern, Text Mining, Search, and Navigation, Microsoft Research