The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, one of two tasks was devoted to learning dependency parsers for a large number of languages, in a realworld setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe data preparation, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

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We present an approach to using a morphological analyzer for tokenizing and morphologically tagging (including partof-speech tagging) Arabic words in one process. We learn classifiers for individual morphological features, as well as ways of using these classifiers to choose among entries from the output of the analyzer. We obtain accuracy rates on all tasks in the high nineties.

Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic's rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.

In this paper, we study the effect of different word-level preprocessing decisions for Arabic on SMT quality. Our results show that given large amounts of training data, splitting off only proclitics performs best. However, for small amounts of training data, it is best to apply English-like tokenization using part-of-speech tags, and sophisticated morphological analysis and disambiguation. Moreover, choosing the appropriate preprocessing produces a significant increase in BLEU score if there is a change in genre between training and test data.