University Calendar

Lecturers: Beata Klebanov & Michael Flor
Educational Testing Service (Princeton)

Topic: Stepping Stones to English Competence -
Idiomatic Expressions in Argumentative Essays Written by Non-Native English Speakers.

• Date: Wednesday, February 15
• Time: 1 p.m.
• Location: Schmitt Hall 104
• Refreshments will be provided
• No RSVP required

We present a work-in-progress that focuses on idiomatic expressions in student essays. We analyzed a corpus of essays written by non-native speakers of English, in response to several different prompt questions for the TOEFL test. We describe a computational procedure for automatic detection of idiom-candidate phrases in essay texts. A subset of the data was manually annotated for idiomatic expressions. In the annotated dataset, we found that some idiomatic expressions are highly topic-specific, while others have more generic usage. We show that topic-specific idioms are often clearly aligned with a certain stance (for or against) and/or with a certain line of argument. This might stem from idioms’ tendency for having strong evaluative (positive or negative) connotations.

Bios:

Beata Beigman Klebanov is a Senior Research Scientist in the Research and Development division at Educational Testing Service in Princeton, NJ.  She received her Ph.D. in computer science in 2008 and her B.S. degree in computer science in 2000—both from The Hebrew University of Jerusalem.  She received her M.S. degree (with distinction) in cognitive science from the University of Edinburgh in 2001. Before joining ETS, she was a post-doctoral fellow at the Northwestern Institute for Complex Systems and Kellogg School of Management where she researched computational approaches to political rhetoric.  Her interests include discourse modeling, analyzing argumentative and figurative language, and automated semantic and pragmatic analysis of text.  At ETS, her focus is on automatically scoring content in student writing.  She researches methods to analyze cohesion in student essays, metaphor, topicality, and sentiment, among others.

Michael Flor is a Research Scientist in the Natural Language Processing group of the ETS R&D division. He earned his PhD in cognitive psychology with specialization in psycholinguistics, from Tel Aviv University, Israel. Michael has also worked as a computational linguist for start-up companies, developing natural-language processing algorithms.  At ETS, Michael specializes in research and systems development for educational applications, focused on automatic processing of text data, combining statistical, linguistic and cognitive approaches.