This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018).
For most languages, morphological and syntactic information – not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe).
This item contains training, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.1 (2018).
The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.1
This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). This is the first release of the corpora without an associated shared task. Previous version (1.2) was associated with the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020). The data covers 26 languages corresponding to the combination of the corpora for all previous three editions (1.0, 1.1 and 1.2) of the corpora. VMWEs were annotated according to the universal guidelines. The corpora are provided in the cupt format, inspired by the CONLL-U format. Morphological and syntactic information, including parts of speech, lemmas, morphological features and/or syntactic dependencies, are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). All corpora are split into training, development and test data, following the splitting strategy adopted for the PARSEME Shared Task 1.2. The annotation guidelines are available online: https://parsemefr.lis-lab.fr/parseme-st-guidelines/1.3 The .cupt format is detailed here: https://multiword.sourceforge.net/cupt-format/
The ACL RD-TEC 2.0 has been developed with the aim of providing a benchmark for the evaluation of methods for terminology extraction and classification as well as entity recognition tasks based on specialised text from the computational linguistics domain. This release of the corpus consists of 300 abstracts from articles in the ACL Anthology Reference Corpus, published between 1978--2006. In these abstracts, terms (i.e., single or multi-word lexical units with a specialised meaning) are manually annotated. In addition to their boundaries in running text, annotated terms are classified into one of the seven categories method, tool, language resource (LR), LR product, model, measures and measurements, and other. To assess the quality of the annotations and to determine the difficulty of this task, more than 171 of the abstracts are annotated twice, independently, by each of the two annotators. In total, 6,818 terms are identified and annotated, resulting in a specialised vocabulary made of 3,318 lexical forms, mapped to 3,471 concepts.