En-Ru translation models, exported via TensorFlow Serving, available in the Lindat translation service (https://lindat.mff.cuni.cz/services/translation/).
The models were trained using the MCSQ social surveys dataset (available at https://repo.clarino.uib.no/xmlui/bitstream/handle/11509/142/mcsq_v3.zip).
Their main use should be in-domain translation of social surveys.
Models are compatible with Tensor2tensor version 1.6.6.
For details about the model training (data, model hyper-parameters), please contact the archive maintainer.
Evaluation on MCSQ test set (BLEU):
en->ru: 64.3 (train: genuine in-domain MCSQ data)
ru->en: 74.7 (train: additional backtranslated in-domain MCSQ data)
(Evaluated using multeval: https://github.com/jhclark/multeval)
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.
MEd is an annotation tool in which linearly-structured annotations of text or audio data can be created and edited. The tool supports multiple stacked layers of annotations that can be interconnected by links. MEd can also be used for other purposes, such as word-to-word alignment of parallel corpora.
On Mediaevum.de, a collection of links to Middle High German texts can be found. These texts are made available via the University of Virginia. Auf Mediaevum.de findet sich eine Linksammlung zu diversen mittelhochdeutschen Texten, welche als Volltexte über die University of Virginia erreichbar sind.
This package provides an evaluation framework, training and test data for semi-automatic recognition of sections of historical diplomatic manuscripts. The data collection consists of 57 Latin charters issued by the Royal Chancellery of 7 different types. Documents were created in the era of John the Blind, King of Bohemia (1310–1346) and Count of Luxembourg. Manuscripts were digitized, transcribed, and typical sections of medieval charters ('corroboratio', 'datatio', 'dispositio', 'inscriptio', 'intitulatio', 'narratio', and 'publicatio') were manually tagged. Manuscripts also contain additional metadata, such as manually marked named entities and short Czech abstracts.
Recognition models are first trained using manually marked sections in training documents and the trained model can then be used for recognition of the sections in the test data. The parsing script supports methods based on Cosine Distance, TF-IDF weighting and adapted Viterbi algorithm.
MBSP is a set of linguistic tools based on the TiMBL and MBT memory based learning applications developed at CNTS and ILK. It provides tools for Part of Speech tagging, Chunking, Lemmatizing, Relation Finding, Named Entity Recognition, and (for medical language) Semantic tagging.
MBT is a memory-based tagger-generator and tagger in one. The tagger-generator part can generate a sequence tagger on the basis of a training set of tagged sequences; the tagger part can tag new sequences. MBT can, for instance, be used to generate part-of-speech taggers or chunkers for natural language processing.
A tool for contrasting terminological vocabularies and textual corpora. It allows controlling the presence and location of reference vocabularies in textual corpora.