Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/).
For each language, automatic annotations in CoNLL-U format are provided in a separate archive. The word embeddings for all languages are distributed in one archive.
Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions.
Update 2018-09-03
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Added data in the 4 “surprise languages” from the 2017 ST: Buryat, Kurmanji, North Sami and Upper Sorbian. This has been promised before, during CoNLL-ST 2018 we gave the participants a link to this record saying the data was here. It wasn't, sorry. But now it is.
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.
The proportional light absorptance by photosynthetic tissue (α) is used with chlorophyll (Chl) fluorescence methods to calculate electron transport rate (ETR). Although a value of α of 0.84 is often used as a standard for calculating ETR, many succulent plant species and species with crassulacean acid metabolism (CAM) have photosynthetic tissues that vary greatly in color or are highly reflective, and could have values of α that differ from 0.84, thus affecting the calculation of ETR. We measured ETR using Chl fluorescence and α using an integrating sphere in 58 plant species to determine the importance of applying a measured value of α when calculating ETR. Values of α varied from 0.55-0.92 with a mean of 0.82 across species. Differences between ETR values calculated with measured α values ranged from 53% lower to 12% greater than ETR values calculated with a standard α value of 0.84 and were significantly different in 39 out of 58 species. While measurements of ETR using Chl fluorescence represent a rapid and effective assessment of physiological performance, the value of α needs to be considered. Measurements of α, especially on species with light-colored or reflective photosynthetic tissue, will allow more accurate determination of photosynthesis in succulent and CAM species. and J. A. Stemke, L. S. Santiago
We exploited leaves of tobacco (Nicotiana tabacum L., cv. Wisconsin 38) with introduced chimeric construct consisting of SAG12 promoter fused with ipt gene for cytokinin synthesis and therefore prolonged life-span. As a control we used its wild type. In 12-week-old plants, the first leaves of control plants showed senescence symptoms at the time of sampling. Carotenoid content decreased with increasing leaf age both in control and in transgenic plants. On the other hand, the first leaves of transgenic plants demonstrated better antioxidant capacity represented by carotenoids compared to the leaves of control plants of the same age. They stayed still green at this age. and D. Procházková, D. Haisel, N. Wilhelmová.