Appel: CL special Issue on Parsing Morphologically Rich Languages (CLPMRL)

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Fri Feb 18 20:39:20 UTC 2011

Date: Wed, 16 Feb 2011 02:12:52 +0100
From: DJamé Seddah <djame.seddah at>
Message-Id: <C651F876-7004-42A8-8380-27CF52E0F2EB at>

**apologies for cross-posting**

2nd Call For Papers: Special issue of the Computational Linguistics
journal on Parsing Morphologically Rich Languages (CLPMRL)


In the context of computational linguistics, parsing is the task of
automatically analyzing the syntactic structure of sentences in
natural language. Although the performance of parsing systems has
improved tremendously in recent years, there is increasing evidence
that performance is sensitive to typological differences between
languages. Thus, statistical models for phrase structure parsing
developed for English often exhibit a drastic drop in performance when
applied to languages such as German, Arabic and Hebrew. Similarly,
multilingual evaluation campaigns for statistical dependency parsers
have shown considerable variation in accuracy that is partly related
to typological characteristics. In both cases, it appears that the
greatest challenges are posed by morphologically rich languages (MRL),
where significant information concerning syntactic structure is
expressed at the word level, where each word can have a very high
number of possible forms, and where word order is weakly constrained
by syntactic structure.  The challenges exhibited by MRLs transcend
language boundaries, and emerging insights are often relevant across
theoretical frameworks and methodological traditions. This special
issue aims to provide the focal point for studies of large-scale,
broad-coverage parsing models that can successfully cope with the
challenges exhibited by MRLs, from both the formal and the statistical
points of view. It sets out to provide an overview of the
state-of-the-art solutions, shared insights across languages and
frameworks, and lessons relevant to downstream applications.


We solicit novel contributions describing completed work on
broad-coverage parsing of morphologically rich languages, from formal
or statistical points of view. The topics to be covered in this issue
include, but are not limited to:

- Parsing models and architectures that explicitly integrate
  morphological and syntactic information

- Cross-language and/or cross-model comparison of models' strengths
  and weaknesses in the face of morphosyntactic phenomena

- Comprehensive analyses of parsing models' performance with respect
  to variation in tag-sets, annotation schemes and data transformation

- Evaluation of parsers involving different frameworks or different
  syntactic theories (e.g. constituency-based or dependency-based) for

- Better models to cope with high variation in word-form and improved
  handling of OOV words, by incorporating linguistic knowledge or
  through automatic learning techniques


In order to provide a wide exposure to the state-of-the-art in the
field, covering multiple frameworks as well as multiple languages, the
editorial board of this special issue will use a new format with
multiple short papers of length up to 25 pages (excluding
references). Submitted papers must follow the CL formatting guidelines
available at


Potential contributors are invited to send an expression of interest
(EOI) to the guest editors by February 20, 2011. The EOIs should
consist of a title, the language(s), and a brief indication of the
topic.  EOIs and inquiries should be directed to the guest editors via
clpmrl [at]


Call for papers:         December 20, 2010
Expression of interest:         February 20, 2011
Submission:         June 1, 2011
Notification:         September 20, 2011
Revision:         December 20, 2011
Final decision:         January 15, 2012
Final version:            February 1, 2012


Reut Tsarfaty (Uppsala University, Sweden)
Djamé Seddah (Alpage & Université Paris Sorbonne, France)
Sandra Kübler (Indiana University, USA)
Joakim Nivre (Uppsala University, Sweden)


Mail: clpmrl [at]

External website (with a faq):

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