The Workshop on Computational Approaches to Causality in Language will provide a forum for presentation and discussion of innovative research on all aspects of recognition, representation and the use of causal information and its processing in NLP-centered applications. The workshop encourages researchers to submit papers (long (8-pages) and short (4-pages)) that report on accomplished and ongoing work related, but not restricted, to the following topics:
- annotation of causal information in text;
- knowledge engineering with causalities;
- reasoning with causal relations;
- question answering with causal questions;
- social media and behavioral causality;
- resources for causal information;
- extracting causal relations in law and legal texts;
- extracting causal relations in clinical texts;
- causal relations in natural language discourse;
- causality for planning in natural text generation and interactive game design.
In addition, the workshop sets the goal of initiating a discussion on computational approaches of recognition of causal relations in text which will lead to a shared task in the scope of Semantic Evaluation Exercises (SemEval).
Each submission will be reviewed by at least three program committee members. Papers must follow the two-column format of EACL-2014 (http://www.eacl2014.org/files/eacl-2014-styles.zip) and may consist of up to eight (8) pages of content (for long papers) and four (4) pages (for short papers), plus two extra pages for references. Final versions should take into account reviewers' comments. Papers will be presented orally or as posters as determined by the program committee. Decisions on presentation format will be based on the nature rather than the quality of the work. There will be no distinction in the proceedings between long papers presented orally and as posters.
Papers must be submitted electronically as a PDF file by uploading onto the START system (https://www.softconf.com/eacl2014/CAtoCL/) no later than January 30, 2014 (UTC-11).
The workshop is supported by the MUSE project (EU FP7-296703) - Machine Understanding for interactive StorytElling.