Overview of the SPMRL 2013 shared task: cross-framework evaluation of parsing morphologically rich languages

This paper reports on the first shared task on statistical parsing of morphologically rich languages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the evaluation metrics for parsing MRLs given different representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios

Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages

International audienceThis paper reports on the first shared task on statistical parsing of morphologically rich lan- guages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the eval- uation metrics for parsing MRLs given dif- ferent representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios

Using an SVM Ensemble System for Improved Tamil Dependency Parsing

Dependency parsing has been shown to improve NLP systems in certain languages and in many cases helps achieve state of the art results in NLP applications, in particular applications for free word order languages. Morphologically rich languages are often short on training data or require much higher amounts of training data due to the increased size of their lexicon. This paper examines a new approach for addressing morphologically rich languages with little training data to start. Using Tamil as our test language, we create 9 dependency parse models with a limited amount of training data. Using these models we train an SVM classifier using only the model agreements as features. We use this SVM classifier on an edge by edge decision to form an ensemble parse tree. Using only model agreements as features allows this method to remain language independent and applicable to a wide range of morphologically rich languages. We show a statistically significant 5.44% improvement over the average dependency model and a statistically significant 0.52% improvement over the best individual system