Ani Nenkova: Style Analysis for Practical Semantic Interpretation of Text

This year's joint CSE/IST Colloquium on Natural Language Processing just commenced with a great start!

Style Analysis for Practical Semantic Interpretation of Text

Traditionally, natural language processing practitioners work under the assumption that the direct goal of text analysis is to ultimately derive a semantic interpretation of text. We explore alternatives to this tradition and instead focus on detecting style differences first, deferring or entirely foregoing semantic interpretation. This ``style, then semantics if need be" approach to understanding reflects typical human behavior and may prove beneficial for many practical applications of language processing. Under style we hope to capture how content is conveyed rather than exactly what facts are being communicated or what truth values one ought to assign to the expressed statements.

Main challenges in style analysis are the lack of clear definition of the required stylistic dimensions and firm understanding of the granularity on which style should be analyzed. Here we present initial task-dependent style analysis in the context of automatic summarization. We present results on word-, sentence- and paragraph-level and show first results connecting style analysis on each of these levels and the performance of an automatic summarizer.

These results are part of a long-term research agenda aiming to establish style analysis as an integral area of computational linguistics research and to elucidate the specific mechanisms via which style modulates and enhances the semantic interpretation of text.

Ani Nenkova is an associate professor of computer and information science at the University of Pennsylvania. Her main areas of research are computational linguistics and artificial intelligence, with emphasis on developing computational methods for analysis of text quality and style, discourse, affect recognition and summarization. She obtained her PhD degree in computer science from Columbia University and was a postdoctoral fellow in linguistics at Stanford University before joining Penn.  Ani and her collaborators are recipients of the best student paper award at SIGDial in 2010 and best paper award at EMNLP-CoNLL in 2012. The Penn team co-led by Ani won the audio-visual emotion recognition challenge (AVEC) for word-level prediction in 2012.

Luckily we also had the chance to hear more about Ani's work by spending time with her during lunch!

Find more about Ani Nenkova here.