WORKSHOP REPORT

Stylistics for Text Retrieval in Practice
SIGIR 2006 Workshop
Seattle, August 10, 2006
Ozlem Uzuner*, Shlomo Argamon**, Jussi Karlgren**** University at Albany, State University of New York, USA** Illinois Institute of Technology, USA*** Swedish Institute for Computer Science, Sweden
ouzuner@albany.edu, argamon@iit.edu, jussi@sics.se

Abstract
Stylistics for Text Retrieval in Practice has met during SIGIR
2006. With participants from both academia and industry, theworkshop spurred interesting discussions on the future ofstylistics, its practical uses, and “killer app”. The paperspresented varied from customer feedback evaluation systems toautomatic speech generation.

1Overview

Recent years have seen an increased attention to various aspects of automatic analysis and extraction ofstylistic aspects of natural language texts. This workshop focused on “style”, i.e., the manner ofexpression of the “content” and was open to submissions on modelling, representing, explaining, andutilizing variation in the manner of expression.
Potentially useful applications of stylistic analysis abound, including systems for genre-based
information retrieval, authorship attribution, plagiarism detection, context-sensitive text or speech
generation systems, organizing and retrieving documents based on their writing style, attitude, orsentiment, quality or appropriateness filters for messaging systems, detecting abusive or threatening
language, and more. This year, participants were expected to bring with them a method for applying
stylistic analysis to information access tasks. In particular, the organizers encouraged working systems
and demonstrations.
Participants were asked to address the following key challenge question in their participation proposals:* WHAT IS A MEANINGFUL "KILLER APP" FOR STYLISTIC TEXT ANALYSIS? *and to consider the following questions for discussion in session:
1. How does style relate to other forms of non-topical textual variation?
2. What features are best for different style analysis tasks?
3. Is cross-lingual or 'universal' style analysis possible, and if so, how?
4. How might we develop useful shared resources for moving style research forward?

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2Submissions

The presentations in this workshop encompassed authorship attribution [1, 3, 6], customer review
evaluation [4, 5], and speech [2]. Akiva et al. presented “a system for insight retrieval out of web pages
using style factors for profile and tone classification”, and demonstrated that style features compoundedwith profile and tone information provides the best results for intelligence [1]. Alias et al. presented atext-to-speech system that can generate natural sounding speech in different “speech styles”, ormoods [2]. Galitsky et al. presented a “Complaint Engine” suit for mediating consumer disputes. The
broad range of applications discussed at this meeting demonstrated the relevance of stylistics to a variety
of applications. Participants discussed the possibility of expanding their stylistic research to closely
related TREC tasks and to the Digital Humanities communities.

3Answers?

The question that proved easiest to answer was the question on sharing resources. The participants
decided to establish a common data and related work repository to serve both the current community andthe future entrants of the field.
In answer to the question on cross-linguality and universality, several of the participants demonstrated
technology that was mainly language-independent: in spite of the highly language specific instantiationsof feature sets, the feature bundles and dimensions of variations were mostly defined in general terms,
readily transferable from one language to another for documents that are within the same genre. This, ofcourse, begs further questions; however, the general sense of the workshop participants is that thetechniques and features used are generalizable.
The style features that proved most useful varied with the task. The participants demonstrated systems
that took advantage of both conventional and idiosyncratic features, including both linguistic knowledge
and surface features. The tasks focused on different types of analysis: obtaining knowledge about
text [2] and author [1, 3, 4, 6], and extracting opinions and review information from text [5].
This led us to the question "What is a meaningful killer app?" — the main challenge question of theworkshop — which remains unanswered. Several different applications were demonstrated at theworkshop and many more mentioned. Given the varying applications, technologies, and researchbackgrounds of the participants, no single answer could be established. The resolution of this issue will
be deferred to future events!

4References[1] Navot Akiva, Johnathan Schler. TrendMine: Utilizing Authorship Profiling and Tone Analysis inContext. In Proceedings of the ACM SIGIR Workshop on Stylistics for Text Retrieval in Practice.
August 2006.
[2] Francesc Alias, Xavier Sevillano, Joan Claudi Socoro. Text Classification based on Associative
Relational Networks for Multi-Domain Text-to-Speech Synthesis. In Proceedings of the ACM SIGIR
Workshop on Stylistics for Text Retrieval in Practice. August 2006.
[3] Shlomo Argamon, Moshe Koppel, James Pennebaker, Jonathan Schler. A Tool for AutomatedAuthorship Attribution. In Proceedings of the ACM SIGIR Workshop on Stylistics for Text Retrieval inPractice. August 2006.

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[4] Boris Galitsky, Boris Kovalerchuk. Analyzing Attitude in Customer Emails: A Tool for Complaint
Assessment. In Proceedings of the ACM SIGIR Workshop on Stylistics for Text Retrieval in Practice.
August 2006.
[5] Xiao Hu, J. Stephen Downie. Stylistics in Customer Reviews of Cultural Objects.
In Proceedingsof the ACM SIGIR Workshop on Stylistics for Text Retrieval in Practice. August 2006.
[6] George Mikros. Authorship Attribution in Modern Greek Newswire Corpora. In Proceedings of theACM SIGIR Workshop on Stylistics for Text Retrieval in Practice. August 2006.

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