PRESENTATION

    The availability of large syntactically annotated corpora led to an explosion of interest in statistical parsing methods, and to the development of successful models for parsing English using the Wall Street Journal Penn Treebank (PTB, Marcus et al, 1993). In recent years, parsing performance on the PTB has reached a performance ceiling of 90-92% f-score using the Parseval evaluation metrics (Black et al, 1991). When adapted to other language/treebank pairs (such as German, Hebrew, Arabic, Italian or French), these models have been shown to be considerably less successful.

    Among the arguments that have been proposed to explain this performance gap are the impact of small training data size, differences in treebank annotation schemes, inadequacy of evaluation metrics, as well as linguistic factors such as the degree of word order freedom and the use of morphological information in the parser. None of these arguments in isolation can account for the systematic performance deterioration, but observed from a wider, cross-linguistic perspective, a picture begins to emerge -- the morphologically rich nature of some of the languages makes them inherently more susceptible to such performance degradation.    

Morphologically rich languages (MRLs) are particularly challenging for the application of algorithms primarily designed to parse English. These algorithms focus on learning word order but they often do not take morphological information into account. Another typical problem associated with parsing MRLs is increased lexical data sparseness due to high morphological variation in surface forms. In a more general setup, this problem is akin to handling out-of-vocabulary or rare words for robust statistical parsing and techniques for domain adaptation via lexicon enhancement (also explored for English and less morphologically rich languages).

As well as technical and linguistic difficulties, lack of communication between researchers working on different MRLs can lead to a reinventing the wheel syndrome; the prominence of English parsing in the literature reduces the visibility of research aiming to solve the problems particular to MRLs. By offering a platform to this growing community of interests we hope to overcome this potential cultural obstacle.

We solicit papers describing parsing experiments with models and architectures for languages with morphological structure richer than English, or studies that address the lexical sparseness challenges (for any language). The workshop's areas of interest include, but are not limited to, the following list of topics:

• parsing models and architectures that explicitly integrate morphological analysis and parsing
• parsing models and architectures that focus on lexical coverage and the handling of OOV words either by incorporating linguistic knowledge or through the use of unsupervised/semi-supervised learning techniques
  
• Cross-language and cross-model comparison of models' strength and weaknesses in the face of particular linguistic phenomena (e.g. morphosyntactic characteristics, degree of word-order freedom ...)
• comprehensive analyses of the strengths and weaknesses of various parsing models on particular linguistic (e.g. morphosyntactic) phenomena with respect to  variation in tagsets, annotation schemes and additional data transformations

ACCEPTED PAPERS 

(Workshop's preface)
STATISTICAL PARSING OF MORPHOLOGICALLY RICH LANGUAGES (SPMRL)

WHAT, HOW AND WHITHER 
Reut Tsarfaty, Djamé Seddah, Yoav Goldberg, Sandra Kuebler, Yannick Versley, Marie Candito, Jennifer Foster, Ines Rehbein and Lamia Tounsi

(Long papers)
APPLICATION OF DIFFERENT TECHNIQUES TO DEPENDENCY PARSING OF BASQUE
Kepa Bengoetxea and Koldo Gojenola

DIRECT PARSING OF DISCONTINUOUS CONSTITUENTS IN GERMAN
Wolfgang Maier

FACTORS AFFECTING THE ACCURACY OF KOREAN PARSING
Tagyoung Chung, Matt Post and Daniel Gildea

HANDLING UNKNOWN WORDS IN STATISTICAL LATENT-VARIABLE PARSING MODELS
FOR ARABIC, ENGLISH AND FRENCH
Mohammed Attia, Jennifer Foster, Deirdre Hogan, Joseph Le Roux, Lamia Tounsi and Josef van Genabith

IMPROVING ARABIC DEPENDENCY PARSING WITH LEXICAL AND INFLECTIONAL MORPHOLOGICAL FEATURES
Yuval Marton, Nizar Habash and Owen Rambow

LEMMATIZATION AND LEXICALIZED STATISTICAL PARSING OF MORPHOLOGICALLY-RICH LANGUAGES: THE CASE OF FRENCH
Djamé Seddah, Grzegorz Chrupala, Ozlem Cetinoglu, Josef van Genabith and Marie Candito

MODELING MORPHOSYNTACTIC AGREEMENT IN CONSTITUENCY-BASED PARSING OF MODERN HEBREW
Reut Tsarfaty and Khalil Sima'an

ON THE ROLE OF MORPHOSYNTACTIC FEATURES IN HINDI DEPENDENCY PARSING
Bharat Ram Ambati, Samar Husain, Joakim Nivre and Rajeev Sangal

PARSING WORD CLUSTERS
Marie Candito and Djamé Seddah

TWO METHODS TO INCORPORATE 'LOCAL MORPHOSYNTACTIC' FEATURES IN HINDI DEPENDENCY PARSING
Bharat Ram Ambati, Samar Husain, Sambhav Jain, Dipti Misra Sharma and Rajeev Sangal

(Short paper)
EASY-FIRST DEPENDENCY PARSING OF MODERN HEBREW
Yoav Goldberg and Michael Elhadad