The method of stylometry by most frequent words does not allow direct comparison of original texts and their translations, i.e. across languages. For instance, in a bilingual Czech-German text collection containing parallel texts (originals and translations in both directions, along with Czech and German translations from other languages), authors would not cluster across languages, since frequency word lists for any Czech texts are obviously going to be more similar to each other than to a German text, and the other way round. We have tried to come up with an interlingua that would remove the language-specific features and possibly keep the linguistically independent features of individual author signal, if they exist. We have tagged, lemmatized, and parsed each language counterpart with the corresponding language model in UDPipe, which provides a linguistic markup that is cross-lingual to a significant extent. We stripped the output of language-dependent items, but that alone did not help much. As a next step, we transformed the lemmas of both language counterparts into shared pseudolemmas based on a very crude Czech-German glossary, with a 95.6% success. We show that, for stylometric methods based on the most frequent words, we can do without translations.

We present a pilot study of machine translation of selected grammatical contrasts between Czech and English in WMT18 News Translation Task. For each phenomenon, we run a dedicated test which checks if the candidate translation expresses the phenomenon as expected or not. The proposed type of analysis is not an evaluation in the strict sense because the phenomenon can be correctly translated in various ways and we anticipate only one. What is nevertheless interesting are the differences between various MT systems and the single reference translation in their general tendency in handling the given phenomenon.

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, the task was devoted to learning dependency parsers for a large number of languages, in a real-world 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 how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

We present coreference annotation on parallel Czech-English texts of the Prague Czech-English Dependency Treebank (PCEDT). The paper describes innovations made to PCEDT 2.0 concerning coreference, as well as coreference information already present there. We characterize the coreference annotation scheme, give the statistics and compare our annotation with the coreference annotation in Ontonotes and Prague Dependency Treebank for Czech. We also present the experiments made using this corpus to improve the alignment of coreferential expressions, which helps us to collect better statistics of correspondences between types of coreferential relations in Czech and English. The corpus released as PCEDT 2.0 Coref is publicly available.

We present VPS-GradeUp ― a set of 11,400 graded human decisions on usage patterns of 29 English lexical verbs from the Pattern Dictionary of English Verbs by Patrick Hanks. The annotation contains, for each verb lemma, a batch of 50 concordances with the given lemma as KWIC, and for each of these concordances we provide a graded human decision on how well the individual PDEV patterns for this particular lemma illustrate the given concordance, indicated on a 7-point Likert scale for each PDEV pattern. With our annotation, we were pursuing a pilot investigation of the foundations of human clustering and disambiguation decisions with respect to usage patterns of verbs in context. The data set is publicly available at http://hdl.handle.net/11234/1-1585.

We present a pilot analysis of a new linguistic resource, VPS-GradeUp (available at http://hdl.handle.net/11234/1-1585). The resource contains 11,400 graded human decisions on usage patterns of 29 English lexical verbs, randomly selected from the Pattern Dictionary of English Verbs (Hanks, 2000 2014) based on their frequency and the number of senses their lemmas have in PDEV. This data set has been created to observe the interannotator agreement on PDEV patterns produced using the Corpus Pattern Analysis (Hanks, 2013). Apart from the graded decisions, the data set also contains traditional Word-Sense-Disambiguation (WSD) labels. We analyze the associations between the graded annotation and WSD annotation. The results of the respective annotations do not correlate with the size of the usage pattern inventory for the respective verbs lemmas, which makes the data set worth further linguistic analysis.

We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP~2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises. For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium in May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations.

We are presenting VPS-30-En, a small lexical resource that contains the following 30 English verbs: access, ally, arrive, breathe, claim, cool, crush, cry, deny, enlarge, enlist, forge, furnish, hail, halt, part, plough, plug, pour, say, smash, smell, steer, submit, swell, tell, throw, trouble, wake and yield. We have created and have been using VPS-30-En to explore the interannotator agreement potential of the Corpus Pattern Analysis. VPS-30-En is a small snapshot of the Pattern Dictionary of English Verbs (Hanks and Pustejovsky, 2005), which we revised (both the entries and the annotated concordances) and enhanced with additional annotations. It is freely available at http://ufal.mff.cuni.cz/spr. In this paper, we compare the annotation scheme of VPS-30-En with the original PDEV. We also describe the adjustments we have made and their motivation, as well as the most pervasive causes of interannotator disagreements.

We introduce a substantial update of the Prague Czech-English Dependency Treebank, a parallel corpus manually annotated at the deep syntactic layer of linguistic representation. The English part consists of the Wall Street Journal (WSJ) section of the Penn Treebank. The Czech part was translated from the English source sentence by sentence. This paper gives a high level overview of the underlying linguistic theory (the so-called tectogrammatical annotation) with some details of the most important features like valency annotation, ellipsis reconstruction or coreference.

EngValLex is the name of an FGD-compliant valency lexicon of English verbs, built from the PropBank-Lexicon and following the structure of Vallex, the FGD-based lexicon of Czech verbs. EngValLex is interlinked with the PropBank-Lexicon, thus preserving the original links between the PropBank-Lexicon and the PropBank-Corpus. Therefore it is also supposed to be part of corpus annotation. This paper describes the automatic conversion of the PropBank-Lexicon into Pre-EngValLex, as well as the progress of its subsequent manual refinement (EngValLex). At the start, the Propbank-arguments were automatically re-labeled with functors (semantic labels of FGD) and the PropBank-rolesets were split into the respective example sentences, which became FGD-valency frames of Pre-EngValLex. Human annotators check and correct the labels and make the preliminary valency frames FGD-compliant. The most essential theoretical difference between the original and EngValLex is the syntactic alternations used by the PropBank-Lexicon, not yet employed within the Czech framework. The alternation-based approach substantially affects the conception of the frame, making in very different from the one applied within the FGD-framework. Preserving the valuable alternation information required special linguistic rules for keeping, altering and re-merging the automatically generated preliminary valency frames.

This work focuses on semi-automatic extraction of verb-noun collocations from a corpus, performed to provide lexical evidence for the manual lexicographical processing of Support Verb Constructions (SVCs) in the Swedish-Czech Combinatorial Valency Lexicon of Predicate Nouns. Efficiency of pure manual extractionprocedure is significantly improved by utilization of automatic statistical methods based lexical association measures.