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.

No abstract

This paper summarizes results of a theoretical analysis of syntactic behavior of Czech light verb constructions and their verification in the linguistic annotation of a large amount of these constructions. The concept of LVCs is based on the observation that nouns denoting actions, states, or properties have a strong tendency to select semantically underspecified verbs, which leads to a specific rearrangement of valency complementations of both nouns and verbs in the syntactic structure. On the basis of the description of deep and surface syntactic properties of LVCs, a formal model of their lexicographic representation is proposed here. In addition, the resulting data annotation, capturing almost 1,500 LVCs, is described in detail. This annotation has been integrated in a new version of the VALLEX lexicon, release 3.5.

In this article, we introduce a project aimed at enhancing a valency lexicon of Czech verbs with semantic information. For this purpose, we make use of FrameNet, a semantically oriented lexical resource. At the present stage, semantic frames from FrameNet have been mapped to eight groups of verbs with various semantic and syntactic properties. The feasibility of this task has been verified by the achieved inter-annotator agreement measured on two semantically and syntactically different groups of verbs -verbs of communication and exchange (85.9% and 78.5%, respectively). Based on the upper level semantic frames from the relation of 'Inheritance' built in FrameNet, the verbs of these eight groups have been classified into more coherent semantic classes. Moreover, frame elements from these upper level semantic frames have been assigned to valency complementations of the verbs of the listed groups as semantic roles. As in case of semantic frames, the achieved interannotator agreement concerning assigning frame elements measured on verbs of communication and exchange has been promising (95.6% and 91.2%, respectively).As a result, 1 270 lexical units pertaining to the verbs of communication, mental action, psych verbs, social interaction, verbs of exchange, motion, transport and location (2 129 Czech verbs in total if perfective and imperfective verbs being counted separately) have been classified into syntactically and semantically coherent classes and their valency complementations have been characterized by semantic roles adopted from the FrameNet lexical database.

No abstract