23.206, Calls: Computational Ling, Text/Corpus Ling, Ling & Lit/Canada

Wed Jan 11 16:56:47 UTC 2012

LINGUIST List: Vol-23-206. Wed Jan 11 2012. ISSN: 1069 - 4875.

Subject: 23.206, Calls: Computational Ling, Text/Corpus Ling, Ling & Lit/Canada

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Date: 10-Jan-2012
From: Anna Kazantseva [ankazant at site.uottawa.ca]
Subject: Workshop on Computational Linguistics for Literature

-------------------------Message 1 ---------------------------------- 
Date: Wed, 11 Jan 2012 11:55:57
From: Anna Kazantseva [ankazant at site.uottawa.ca]
Subject: Workshop on Computational Linguistics for Literature

E-mail this message to a friend:
Full Title: Workshop on Computational Linguistics for Literature 

Date: 07-Jun-2012 - 08-Jun-2012
Location: Montréal, Québec, Canada 
Contact Person: Anna Kazantseva
Meeting Email: clfl.workshop at gmail.com
Web Site: https://sites.google.com/site/clfl2012/ 

Linguistic Field(s): Computational Linguistics; Ling & Literature; Text/Corpus Linguistics 

Call Deadline: 12-Mar-2012 

Meeting Description:

Workshop on Computational Linguistics for Literature
Co-located with
The 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
June 7 or 8, 2012
Montréal, Québec, Canada

All information, including announcements and updates, can be found on the workshop's website:


The amount of literary material available online keeps growing rapidly. Not only are there machine-readable texts in libraries, collections and e-book stores, but there is also more and more 'live' literature - e-zines, blogs, self-published e-books and so on. There is a need for tools to help users navigate, visualize and appreciate high volume of available literature.

Literary texts are quite different from technical and formal documents, which have been the focus of NLP research thus far. Most forms of statistical language processing rely on lexical information in one way or another. In literature, the primary mode is narrative rather than exposition. Stories may be cognitively easier to read than certain expository genres, such as scientific documents, but it is a challenging form of discourse for NLP tools and methods. For instance, literary prose lacks overt lexical clues and structural markers typically leveraged in the processing of more structured genres. Also, even conventional literary texts exhibit far less unity of time, space and topic than most formal discourse. Learning to handle these challenges in literary data may help move past heavy reliance on surface clues in general.

Literature also differs from other genres because of the needs of its typical audience. For instance, reading, searching or browsing literature online is a different task than searching for the latest news on a particular topic. Search criteria would be rather abstract: not a keyword, but a literary style, similarity to another work, point of view and so on. When looking for a summary or a digest, a reader may prefer to know or visualize a text's broad characteristics than facts which summarize the plot. 

Call for Papers:

We invite papers that touch upon these areas, but also welcome other ideas which promote the processing of literary narrative or related forms of discourse.

Topics of Interest:

Note: Papers on other closely related topics will also be considered.

- The needs of the readers and how those needs translate into meaningful NLP tasks
- Searching for literature
- Recommendation systems for literature
- Computational modelling of narratives, computational narratology
- Summarization of literature
- Differences between literature and other genres as relevant to computational linguistics
- Discourse structure in literature
- Emotion analysis for literature
- Profiling and authorship attribution
- Identification and analysis of literature genres
- Building and analysing social networks of characters
- Generation of literary narrative, dialogue or poetry
- Modelling dialogue literary style for generation


We invite submission of long and short papers, describing completed or ongoing research on systems, studies, theories and models which can inform the area of computational linguistics for literature. Long papers should be at most 8 pages, plus unlimited space for references. Short papers should be at most 4 pages plus references, and can be appropriate for either oral or poster presentation. Accepted long papers, and perhaps selected short papers, will be presented as talks. In addition, we encourage submission of position papers - mapping out research ideas and programs - of up to 6 pages plus references.

There will be double-blind review: submissions must be anonymized.

Style files and sample PDFs are available on this page:


Submission Page: 


Important Dates (all deadlines 11:59 pm. Hawaii Time):

Submission deadline: March 12, 2012
Notification of acceptance: April 13, 2012
Camera-ready version due: May 1, 2012
Workshop: June 8, 2012

Program Committee:

Cecilia Ovesdotter Alm (Rochester Institute of Technology)
Nicholas Dames (Columbia University)
Hal Daumé III (University of Maryland)
Anna Feldman (Montclair State University)
Mark Finlayson (MIT)
Pablo Gervás (Universidad Complutense de Madrid)
Roxana Girju (University of Illinois at Urbana-Champaign)
Amit Goyal (University of Maryland)
Katherine Havasi (MIT Media Lab)
Matthew Jockers (Stanford University)
James Lester (North Carolina State University)
Inderjeet Mani (Children's Organization of Southeast Asia)
Kathy McKeown (Columbia University)
Saif Mohammad (National Research Council, Canada)
Vivi Nastase (HITS gGmbH)
Rebecca Passonneau (Columbia University)
Livia Polanyi (LDM Associates)
Owen Rambow (Columbia University)
Michaela Regneri (Saarland University)
Reid Swanson (University of California, Santa Cruz)
Marilyn Walker (University of California, Santa Cruz)
Janice Wiebe (University of Pittsburgh)


David Elson (Google)
Anna Kazantseva (University of Ottawa)
Rada Mihalcea (University of North Texas)
Stan Szpakowicz (University of Ottawa)

Contact Information:

Send general inquiries to clfl.workshop at gmail.com.

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