Editorial: Computational Linguistics and Literature

Stan Szpakowicz, Anna Feldman, Anna Kazantseva

Abstract: Computational Linguistics---or, more technically, Natural Language Processing---has made great strides in the past several years. Machine learning (ML) is the technology of choice; deep learning, in particular, has pushed the envelope. These methods work best in narrow domains, given vast amounts of data and asked for information rather than for interpretation. Literary data are not limited by topic, and they are hardly ever plentiful enough. That is why work on such data has been, as yet, on the periphery of research and development in Computational Linguistics. This research topic aims to bring the processing of literary data to the attention of a broader audience.The early versions of the articles we present here have been published at ACL workshops on Computational Linguistics for Literature held in \href{https://sites.google.com/site/clfl2015/}{2015} and \href{https://sites.google.com/site/clfl2016/}{2016}.\footnote{A previous \href{http://csli-lilt.stanford.edu/ojs/index.php/LiLT/issue/view/5}{special issue} builds upon selected papers from the workshops held in \href{https://sites.google.com/site/clfl2012/}{2012}, \href{https://sites.google.com/site/clfl2013/}{2013} and \href{https://sites.google.com/site/clfl2014a/}{2014}.} The papers offer a representative enough sample of the research brought to those workshops.Antonio Toral, Martijn Wieling and Andy Way tackle a weakness of an ostensibly successful major application, Machine Translation (MT). The raw results---e...