MEBA is a lexical aligner, implemented in C#, based on an iterative algorithm that uses pre-processing steps: sentence alignment ([[http://www.clarin.eu/tools/sal-sentence-aligner|SAL]]), tokenization, POS-tagging and lemmatization (through [[http://www.clarin.eu/tools/ttl-tokenizing-tagging-and-lemmatizing-free-running-texts|TTL]], sentence chunking. Similar to YAWA aligner, MEBA generates the links step by step, beginning with the most probable (anchor links). The links to be
added at any later step are supported or restricted by the links created in the previous iterations. The aligner has different weights and different significance thresholds on each feature and iteration. Each of the iterations can be configured to align different categories of tokens (named entities, dates and numbers, content words, functional words, punctuation) in decreasing order of statistical evidence.
MEBA has an individual F-measure of 81.71% and it is currently integrated in the platform [[http://www.clarin.eu/tools/cowal-combined-word-aligner|COWAL]].
More detailed descriptions are available in [[http://www.racai.ro/~tufis/papers|the following papers]]:
-- Dan Tufiş (2007). Exploiting Aligned Parallel Corpora in Multilingual Studies and Applications. In Toru Ishida, Susan R. Fussell, and Piek T.J.M. Vossen (eds.), Intercultural Collaboration. First International Workshop (IWIC 2007), volume 4568 of Lecture Notes in Computer Science, pp. 103-117. Springer-Verlag, August 2007. ISBN 978-3-540-73999-9.
-- -- Dan Tufiş, Radu Ion, Alexandru Ceauşu, and Dan Ştefănescu (2006). Improved Lexical Alignment by Combining Multiple Reified Alignments. In Toru Ishida, Susan R. Fussell, and Piek T.J.M. Vossen (eds.), Proceedings of the 11th Conference EACL2006, pp. 153-160, Trento, Italy, April 2006. Association for Computational Linguistics. ISBN 1-9324-32-61-2.
-- Dan Tufiş, Radu Ion, Alexandru Ceauşu, and Dan Ştefănescu (2005). Combined Aligners. In Proceedings of the ACL Workshop on Building and Using Parallel Texts: Data-Driven Machine Translation and Beyond, pp. 107-110, Ann Arbor, USA, June 2005. Association for Computational Linguistics. ISBN 978-973-703-208-9.
This multilingual resource contains corpora for 14 languages, gathered at the occasion of the 1.2 edition of the PARSEME Shared Task on semi-supervised Identification of Verbal MWEs (2020). These corpora were meant to serve as additional "raw" corpora, to help discovering unseen verbal MWEs.
The corpora are provided in CONLL-U (https://universaldependencies.org/format.html) format. They contain morphosyntactic annotations (parts of speech, lemmas, morphological features, and syntactic dependencies). Depending on the language, the information comes from treebanks (mostly Universal Dependencies v2.x) or from automatic parsers trained on UD v2.x treebanks (e.g., UDPipe).
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).
For the 1.2 shared task edition, the data covers 14 languages, for which 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 – 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 1.2 (2020). The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.2
This resource is a set of 14 vector spaces for single words and Verbal Multiword Expressions (VMWEs) in different languages (German, Greek, Basque, French, Irish, Hebrew, Hindi, Italian, Polish, Brazilian Portuguese, Romanian, Swedish, Turkish, Chinese).
They were trained with the Word2Vec algorithm, in its skip-gram version, on PARSEME raw corpora automatically annotated for morpho-syntax (http://hdl.handle.net/11234/1-3367).
These corpora were annotated by Seen2Seen, a rule-based VMWE identifier, one of the leading tools of the PARSEME shared task version 1.2.
VMWE tokens were merged into single tokens.
The format of the vector space files is that of the original Word2Vec implementation by Mikolov et al. (2013), i.e. a binary format.
For compression, bzip2 was used.