Morphologically Rich Languages (MRLs) are languages in which grammatical relations such as Subject, Predicate, Object, etc., are indicated morphologically (e.g. through inflection) instead of positionally (as in, e.g. English), and the position of words and phrases in the sentence may vary substantially. The tight connection between the morphology of words and the grammatical relations between them, and the looser connection between the position and grouping of words to their syntactic roles, pose serious challenges for syntactic and semantic processing. Furthermore, since grammatical relations provide the interface to compositional semantics, morpho-syntactic phenomena may significantly complicate processing the syntax--semantics interface. In statistical parsing, which has been a cornerstone of research in NLP and had seen great advances due to the widespread availability of syntactically annotated corpora, English parsing performance has reached a high plateau in certain genres, which is however not always indicative of parsing performance in MRLs, dependency-based and constituency-based alike . Semantic processing of natural language has similarly seen much progress in recent years. However, as in parsing,  the bulk of the work has concentrated on English, and MRLs may present processing challenges that the community is as of yet unaware of, and which current semantic processing technologies may have difficulty coping with. These challenges may lurk in areas where parses may be used as input, such as semantic role labeling, distributional semantics, paraphrasing and textual entailments, or where inadequate pre-processing of morphological variation hurts parsing and semantic tasks alike.

This joint workshop aims to build upon the first and second SPMRL workshops (at NAACL-HLT 2010 and IWPT 2011, respectively) while extending the overall scope to include semantic processing where MRLs pose challenges for algorithms or models initially designed to process English. In particular, we seek to explore the use of newly available syntactically and/or semantically annotated corpora, or data sets for semantic evaluation that can contribute to our understanding of the difficulty that such phenomena pose. One goal of this workshop is to encourage cross-fertilization among researchers working on different languages and among those working on different levels of processing. Of particular interest is work addressing the lexical sparseness and out-of-vocabulary (OOV) issues that occur in both syntactic and semantic processing.

Submission deadline (Long papers):                                     April 18, 2012  (PDT, GMT-8) 

Submission deadline (Short papers -- syntactic parsing):   April 22, 2012  (PDT, GMT-8)
Submission deadline (Short papers -- semantic processing and syntax-semantics interface):
     - initial abstract:                                                             April 23, 2012  (PDT, GMT-8)
     - entire short paper:                                                        April 28, 2012  (PDT, GMT-8)
Notification to authors:    May 12, 2012
Camera ready copy:          May 20, 2012 

The workshop will be organised around three broad themes:

Syntactic Models:  Models and architectures that explicitly integrate morphological analysis and parsing; Cross-language and cross-model comparison of strengths and weaknesses regarding particular linguistic phenomena.

Semantic Models:  State-of-the-art semantic analysis and generation methods for MRLs, including semantic similarity and entailment criteria and their task-specific instantiation, and suitable representations for semantic tasks in MRLs.

Joint Modeling Aspects: Improving lexical coverage and handling of out-of-vocabulary (OOV) words by utilising lexical knowledge or unsupervised/semi-supervised learning techniques; The role of parsing in semantic analysis for MRLs; Pre-processing issues that jointly affect parsing and semantic analysis; Syntax-Semantics interfaces for monolingual or multilingual systems.

The areas of interest for this joint workshop include, but are not limited to, the following topics: 

 Syntactic Parsing of MRLs

• 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' strengths and weaknesses in the face of particular linguistic phenomena (e.g. morpho-syntactic characteristics, degree of word-order freedom ...) 
• comprehensive analyses of the strengths and weaknesses of various parsing models on particular linguistic (e.g. morpho-syntactic) phenomena with respect to variation in tagsets, annotation schemes and additional data transformations 

Semantic Processing of MRLs

• semantic distance and entailment criteria in the MRL space (e.g., with respect to inflection, derivation, root, pattern, lemma, tense, and/or aspect, etc.); possibly task-specific criteria
• lexical resources and morphological analysis tools facilitating semantic distance measures and semantic relation detection
• methods and models for semantic similarity/distance calculation, clustering and paraphrasing relying on MRL properties, and using: probability, vector/graph representation, data-driven and/or linguistic rules, pivoting/SMT, machine-learning, etc.
• paraphrase and textual entailment detection or generation, specific to MRLs (e.g., task-specific issues of inclusion or exclusion of certain paraphrase and textual entailment patterns differing in inflection)
• use of morphological analysis for semantic calculation aimed at reducing sparsity / OOV rate, preferably without losing information due to mere lemmatization
• semantic role labeling (SRL) for MRLs; verbal/nominalized selectional preferences
  

The Syntax-Semantics Interface:

• parsing-based semantic processing tasks, e.g., semantic role labeling (SRL)
• processing of compounds and multi-morphemic words: optimal level(s) of tokenization, representation, and morphological analysis for either/both tasks
• syntax-aware semantic distance measures, paraphrasing and textual entailment
• semantic classes and/or relations as input to syntactic parsing