We present version 1.3 of the PARSEME multilingual corpus annotated with verbal multiword expressions. Since the previous version, new languages have joined the undertaking of creating such a resource, some of the already existing corpora have been enriched with new annotated texts, while others have been enhanced in various ways. The PARSEME multilingual corpus represents 26 languages now. All monolingual corpora therein use Universal Dependencies v.2 tagset. They are (re-)split observing the PARSEME v.1.2 standard, which puts impact on unseen VMWEs. With the current iteration, the corpus release process has been detached from shared tasks; instead, a process for continuous improvement and systematic releases has been introduced.

The paper gives an account of an infrastructure that will be integrated into a platform aimed at providing a multi-faceted experience to visitors of Northern Greece using mythology as a starting point. This infrastructure comprises a multi-lingual and multi-modal corpus (i.e., a corpus of textual data supplemented with images, and video) that belongs to the humanities domain along with a dedicated database (content management system) with advanced indexing, linking and search functionalities. We will present the corpus itself focusing on the content, the methodology adopted for its development, and the steps taken towards rendering it accessible via the database in a way that also facilitates useful visualizations. In this context, we tried to address three main challenges: (a) to add a novel annotation layer, namely geotagging, (b) to ensure the long-term maintenance of and accessibility to the highly heterogeneous primary data – even after the life cycle of the current project – by adopting a metadata schema that is compatible to existing standards; and (c) to render the corpus a useful resource to scholarly research in the digital humanities by adding a minimum set of linguistic annotations.

Large coverage lexical resources that bear deep linguistic information have always been considered useful for many natural language processing (NLP) applications including Machine Translation (MT). In this respect, Frame-based resources have been developed for many languages following Frame Semantics and the Berkeley FrameNet project. However, to a great extent, all those efforts have been kept fragmented. Consequentially, the Global FrameNet initiative has been conceived of as a joint effort to bring together FrameNets in different languages. The proposed paper is aimed at describing ongoing work towards developing the Greek (EL) counterpart of the Global FrameNet and our efforts to contribute to the Shared Annotation Task. In the paper, we will elaborate on the annotation methodology employed, the current status and progress made so far, as well as the problems raised during annotation.

We present edition 1.2 of the PARSEME shared task on identification of verbal multiword expressions (VMWEs). Lessons learned from previous editions indicate that VMWEs have low ambiguity, and that the major challenge lies in identifying test instances never seen in the training data. Therefore, this edition focuses on unseen VMWEs. We have split annotated corpora so that the test corpora contain around 300 unseen VMWEs, and we provide non-annotated raw corpora to be used by complementary discovery methods. We released annotated and raw corpora in 14 languages, and this semi-supervised challenge attracted 7 teams who submitted 9 system results. This paper describes the effort of corpus creation, the task design, and the results obtained by the participating systems, especially their performance on unseen expressions.

This paper describes the PARSEME Shared Task 1.1 on automatic identification of verbal multiword expressions. We present the annotation methodology, focusing on changes from last year’s shared task. Novel aspects include enhanced annotation guidelines, additional annotated data for most languages, corpora for some new languages, and new evaluation settings. Corpora were created for 20 languages, which are also briefly discussed. We report organizational principles behind the shared task and the evaluation metrics employed for ranking. The 17 participating systems, their methods and obtained results are also presented and analysed.

Multiword expressions (MWEs) are known as a “pain in the neck” for NLP due to their idiosyncratic behaviour. While some categories of MWEs have been addressed by many studies, verbal MWEs (VMWEs), such as to take a decision, to break one’s heart or to turn off, have been rarely modelled. This is notably due to their syntactic variability, which hinders treating them as “words with spaces”. We describe an initiative meant to bring about substantial progress in understanding, modelling and processing VMWEs. It is a joint effort, carried out within a European research network, to elaborate universal terminologies and annotation guidelines for 18 languages. Its main outcome is a multilingual 5-million-word annotated corpus which underlies a shared task on automatic identification of VMWEs. This paper presents the corpus annotation methodology and outcome, the shared task organisation and the results of the participating systems.

The paper reports on completed work aimed at the creation of a resource, namely, the Greek Textual Entailment Corpus (GTEC) that is appropriate for guiding training and evaluation of a system that recognizes Textual Entailment in Greek texts. The corpus of textual units was collected in view of a range of NLP applications, where semantic interpretation is of paramount importance, and it was manually annotated at the level of Textual Entailment. Moreover, a number of linguistic annotations were also integrated that were deemed useful for prospect system developers. The critical issue was the development of a final resource that is re-usable and adaptable to different NLP systems, in order to either enhance their accuracy or to evaluate their output. We are hereby focusing on the methodological issues underpinning data selection and annotation. An initial approach towards the development of a system catering for the automatic Recognition of Textual Entailment in Greek is also presented and preliminary results are reported.

This paper reports on the multilingual Language Resources (MLRs), i.e. parallel corpora and terminological lexicons for less widely digitally available languages, that have been developed in the INTERA project and the methodology adopted for their production. Special emphasis is given to the reality factors that have influenced the MLRs development approach and their final constitution. Building on the experience gained in the project, a production model has been elaborated, suggesting ways and techniques that can be exploited in order to improve LRs production taking into account realistic issues.

The paper reports on the development methodology of a system aimed at multi-domain multi-lingual recognition and classification of names in texts, the focus being on the linguistic resources used for training and testing purposes. The corpus presented here has been collected and annotated in the framework of different projects the critical issue being the development of a final resource that is homogenous, re-usable and adaptable to different domains and languages with a view to robust multi-domain and multi-lingual NERC.