We use six benchmark datasets 1 2, including Corel5k , Mirflickr , Espgame , Iaprtc12 , Pascal07 and EURLex-4K . The feature of DensesiftV3h1, HarrishueV3h1 and HarrisSift in the first five datasets are chosen and the corresponding feature dimensions of three views are 3000,300,1000, respectively.

Introduction. The EUR-Lex text collection is a collection of documents about European Union law. It contains many different types of documents, including treaties, legislation, case-law and legislative proposals, which are indexed according to several orthogonal categorization schemes to allow for multiple search facilities.

It contains many different types of documents, including treaties, legislation, case-law and legislative proposals, which are indexed according to several orthogonal categorization schemes to allow for multiple search facilities. We will use Eurlex-4K as an example. In the ./datasets/Eurlex-4K folder, we assume the following files are provided: X.trn.npz: the instance TF-IDF feature matrix for the train set. The data type is scipy.sparse.csr_matrix of size (N_trn, D_tfidf), where N_trn is the number of train instances and D_tfidf is the number of features. This dataset provides statistics on EUR-Lex website from two views: type of content and number of legal acts available. It is updated on a daily basis. 1) The statistics on the content of EUR-Lex (from 1990 to 2018) show a) how many legal texts in a given language and document format were made available in EUR-Lex in a particular month and year.

Stödordningens namn eller namnet på det företag som får ett enskilt stöd: ”Assistenza tecnica nel settore zootecnico” (tekniskt stöd inom eur-lex.europa.eu. Ska artikel 56.1 i fördraget om upprättandet av Europeiska gemenskapen jämförd med artikel 58 i fördraget om upprättandet av Europeiska eur-lex.europa.eu. Det betyder att endast vissa bestämmelser i det allmänna arbetstidsdirektivet (rådets direktiv 93/104/EG av den 23 november 1993 om eur-lex.europa.eu. I kommissionens vitbok Energi för framtiden: förnybara energikällor (1 ) presenteras en startkampanj som syftar till att främja och skynda på EUR-Lex offers access to EU law, case-law by the Court of Justice of the European Union and other public EU documents as well as the authentic electronic Official Journal of the EU – in 24 languages. The zip file containing the source code also includes the EURLex-4K dataset with 1024 dimensional XML-CNN features, as a toy example. To run Slice on the EURLex-4K dataset, execute "bash sample_run.sh" (Linux) or "sample_run" (Windows) in the Slice folder. You should get at Precision@1 of 77.7% if everything is working correctly.

Also, we use least squares regressors for other compared methods (hence, it is a fair For datasets with small labels like Eurlex-4k, Amazoncat-13k and Wiki10-31k, each label clusters contain only one label and we can get each label scores in label recalling part. For ensemble, we use three different transformer models for Eurlex-4K, Amazoncat-13K and Wiki10-31K, and use three different label clusters with BERT Devlin et al.

when comparing the proposed LLSL to other deep learning models, our model steadily shows superior. 3Bibtex, Delicious, EURLex-4K, and Wiki10-31K.

N@1. eur-lex.europa.eu. (b) sodium benzoate as a product market separate from sorbates while leaving open whether potassium benzoate and calcium benzoate are podrán autorizar el envasado al vacío de los cortes de los códigos INT 12, 13, 14 , 15, 16, 17 y 19, en vez del envoltorio individual contemplado en el punto 1. eur- holdings in the capital of the Banca d'Italia, the choice of [] placing them in the foundation was not even available. eur-lex.europa.eu.

EUR-Lex offers access to EU law, case-law by the Court of Justice of the European Union and other public EU documents as well as the authentic electronic Official Journal of the EU – in 24 languages.

Method P@1 P@3 P@5 N@1 N@3 N@5 PSP@1 PSP@3 PSP@5 PSN@1 PSN@3 PSN@5 Model size (GB) Train time (hr) AnnexML * 79.26: 64.30: 52.33: 79.26: 68.13: 61.60: 34 We will use Eurlex-4K as an example. In the ./datasets/Eurlex-4K folder, we assume the following files are provided: X.trn.npz: the instance TF-IDF feature matrix for the train set. The data type is scipy.sparse.csr_matrix of size (N_trn, D_tfidf), where N_trn is the number of train instances and D_tfidf is the number of features.

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( 2018 ) for Wiki-500K and Amazon-670K. EurLex-4K 3993 5.31 15539 5000 AmazonCat-13K 13330 5.04 1186239 203882 Wiki10-31K 30938 18.64 14146 101938 We use simple least squares binary classiﬁers for training and prediction in MLGT. This is because, this classiﬁer is extremely simple and fast.
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For example, to reproduce the results on the EURLex-4K dataset: omikuji train eurlex_train.txt --model_path ./model omikuji test ./model eurlex_test.txt --out_path predictions.txt Python Binding. A simple Python binding is also available for training and prediction. It …

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Download Dataset (Eurlex-4K, Wiki10-31K, AmazonCat-13K, Wiki-500K) Change directory into ./datasets folder, download and unzip each dataset.

eur-lex.europa.eu. eur-lex.europa.eu. Hortalizas cultivadas por su raíz, bulbo o tubérculo (excepto las patatas). eur-lex. europa.eu.

We use six benchmark datasets 1 2, including Corel5k , Mirflickr , Espgame , Iaprtc12 , Pascal07 and EURLex-4K . The feature of DensesiftV3h1, HarrishueV3h1 and HarrisSift in the first five datasets are chosen and the corresponding feature dimensions of three views are 3000,300,1000, respectively.

Line 4 is for smaller datasets, MediaMill, Bibtex, and EUR-Lex and it was fixed to 0.1 for all bigger datasets. EURLex-4K. N@1. eur-lex.europa.eu. (b) sodium benzoate as a product market separate from sorbates while leaving open whether potassium benzoate and calcium benzoate are podrán autorizar el envasado al vacío de los cortes de los códigos INT 12, 13, 14 , 15, 16, 17 y 19, en vez del envoltorio individual contemplado en el punto 1.

We consider four multi-label text classification datasets downloaded from the publicly available Extreme Classification Repository for which we had access to the raw text representation, namely Eurlex-4K, Wiki10-28K, AmazonCat-13K and Wiki-500K. KTXMLC constructs multi-way multiple trees using a parallel clustering algorithm, which leads to fast computational cost. KTXMLC outperforms over the existing tree based classifier in terms of ranking based measures on six datasets named Delicious, Mediamill, Eurlex-4K, Wiki10-31K, AmazonCat-13K, Delicious-200K. We conducted experiments on five standard benchmark datasets, including three medium-scale datasets, EURLex-4k, AmazonCat-13k and Wiki10-31k, and two large-scale datasets, Wiki-500k and Amazon-670k. Table 1 shows the statistics of these datasets. Eurlex-4K, AmazonCat-13K or the Wikipedia-500K, all of them available in the Extreme Classi cation Repository [15].