Comprehensive Arabic LEMmas is a lexicon covering a large list of Arabic lemmas and their corresponding inflected word forms (stems) with details (POS + Root). Each lexical entry represents a lemma followed by all its possible stems and each stem is enriched by its morphological features especially the root and the POS.
It is composed of 164,845 lemmas representing 7,200,918 stems, detailed as follow:
757 Arabic particles
2,464,631 verbal stems
4,735,587 nominal stems
The lexicon is provided as an LMF conformant XML-based file in UTF8 encoding, which represents about 1,22 Gb of data.
Citation:
– Namly Driss, Karim Bouzoubaa, Abdelhamid El Jihad, and Si Lhoussain Aouragh. “Improving Arabic Lemmatization Through a Lemmas Database and a Machine-Learning Technique.” In Recent Advances in NLP: The Case of Arabic Language, pp. 81-100. Springer, Cham, 2020.
Baseline UDPipe models for CoNLL 2017 Shared Task in UD Parsing, and supplementary material.
The models require UDPipe version at least 1.1 and are evaluated using the official evaluation script.
The models are trained on a slightly different split of the official UD 2.0 CoNLL 2017 training data, so called baselinemodel split, in order to allow comparison of models even during the shared task. This baselinemodel split of UD 2.0 CoNLL 2017 training data is available for download.
Furthermore, we also provide UD 2.0 CoNLL 2017 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.
Finally, we supply all required data and hyperparameter values needed to replicate the baseline models.
Baseline UDPipe models for CoNLL 2018 Shared Task in UD Parsing, and supplementary material.
The models require UDPipe version at least 1.2 and are evaluated using the official evaluation script. The models were trained using a custom data split for treebanks where no development data is provided. Also, we trained an additional "Mixed" model, which uses 200 sentences from every training data. All information needed to replicate the model training (hyperparameters, modified train-dev split, and pre-computed word embeddings for the parser) are included in the archive.
Additionaly, we provide UD 2.2 CoNLL 2018 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.
Tokenizer, POS Tagger, Lemmatizer, and Parser model based on the PDT-C 1.0 treebank (https://hdl.handle.net/11234/1-3185). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model . To use these models, you need UDPipe version 2.1, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .
Lexicon of Czech verbal multiword expressions (VMWEs) used in Parseme Shared Task 2017. https://typo.uni-konstanz.de/parseme/index.php/2-general/142-parseme-shared-task-on-automatic-detection-of-verbal-mwes
Lexicon consists of 4785 VMWEs, categorized into four categories according to Parseme Shared Task (PST) typology: IReflV (inherently reflexive verbs), LVC (light verb constructions), ID (idiomatic expressions) and OTH (other VMWEs with other than verbal syntactic head).
Verbal multiword expressions as well as deverbative variants of VMWEs were annotated during the preparation phase of PST. These data were published as http://hdl.handle.net/11372/LRT-2282. Czech part includes 14,536 VMWE occurences:
1611 ID
10000 IReflV
2923 LVC
2 OTH
This lexicon was created out of Czech data. Each lexicon entry is represented by one line in the form:
type lemmas frequency PoS [used form 1; used form 2; ... ]
(columns are separated by tabs) where:
type ... is the type of VMWE in PST typology
lemmas ... are space separated lemmatized forms of all words that constitutes the VMWE
frequency ... is the absolute frequency of this item in PST data
PoS ... is a space separated list of parts of speech of individual words (in the same order as in "lemmas")
final field contains a list of all (1 to 18) used forms found in the data (since Czech is a flective language).
POS Tagger and Lemmatizer models for EvaLatin2020 data (https://github.com/CIRCSE/LT4HALA). The model documentation including performance can be found at https://ufal.mff.cuni.cz/udpipe/2/models#evalatin20_models .
To use these models, you need UDPipe version at least 2.0, which you can download from https://ufal.mff.cuni.cz/udpipe/2 .
Indonesian text corpus from web. Crawling done by SpiderLing in 2017. Filtering by JusText and Onion (see http://corpus.tools/ for details). Tagged and lemmatized by MorphInd (http://septinalarasati.com/morphind/).
The goal of this paper is to provide an overview of the structure and contents of the soon-to-be available ORAL corpus, which combines previously published corpora (ORAL2006, ORAL2008 and ORAL2013) with newly transcribed material into a single conveniently accessible and more richly annotated resource, about 6 million running words in length. The recordings and corresponding transcripts span a decade between 2002 and 2011; most of them capture interactions of mutually well-acquainted speakers, in informal situations and natural settings. The corpus is complemented by amarginal portion of more formal data, mostly public talks. It is tagged and lemmatized, and an effort was made to adapt existing tools (targeted at written language) to yield better results on spoken data. We hope the availability of such a resource will spawn further discussions on the morphological and syntactic analysis of spoken language, perhaps resulting in more radical departures in the future from the part-of-speech classification inherited from the linguistic analysis of written language.
Model trained for Czech POS Tagging and Lemmatization using Czech version of BERT model, RobeCzech. Model is trained on data from Prague Dependency Treebank 3.5. Model is a part of Czech NLP with Contextualized Embeddings master thesis and presented a state-of-the-art performance on the date of submission of the work.
Demo jupyter notebook is available on the project GitHub.