The Universal Dependencies (UD) project seeks to develop cross-linguistically consistent treebank annotation of morphology and syntax for multiple languages. The first version of the dataset was released in 2015 and consisted of 10 treebanks over 10 languages. Version 2.7 released in 2020 consists of 183 treebanks over 104 languages. The annotation consists of UPOS (universal part-of-speech tags), XPOS (language-specific part-of-speech tags), Feats (universal morphological features), Lemmas, dependency heads and universal dependency labels.
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The shared task of CoNLL-2002 concerns language-independent named entity recognition. The types of named entities include: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task were offered training and test data for at least two languages. Information sources other than the training data might have been used in this shared task.
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English Web Treebank is a dataset containing 254,830 word-level tokens and 16,624 sentence-level tokens of webtext in 1174 files annotated for sentence- and word-level tokenization, part-of-speech, and syntactic structure. The data is roughly evenly divided across five genres: weblogs, newsgroups, email, reviews, and question-answers. The files were manually annotated following the sentence-level tokenization guidelines for web text and the word-level tokenization guidelines developed for English treebanks in the DARPA GALE project. Only text from the subject line and message body of posts, articles, messages and question-answers were collected and annotated.
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XGLUE is an evaluation benchmark XGLUE,which is composed of 11 tasks that span 19 languages. For each task, the training data is only available in English. This means that to succeed at XGLUE, a model must have a strong zero-shot cross-lingual transfer capability to learn from the English data of a specific task and transfer what it learned to other languages. Comparing to its concurrent work XTREME, XGLUE has two characteristics: First, it includes cross-lingual NLU and cross-lingual NLG tasks at the same time; Second, besides including 5 existing cross-lingual tasks (i.e. NER, POS, MLQA, PAWS-X and XNLI), XGLUE selects 6 new tasks from Bing scenarios as well, including News Classification (NC), Query-Ad Matching (QADSM), Web Page Ranking (WPR), QA Matching (QAM), Question Generation (QG) and News Title Generation (NTG). Such diversities of languages, tasks and task origin provide a comprehensive benchmark for quantifying the quality of a pre-trained model on cross-lingual natural lan
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GUM is an open source multilayer English corpus of richly annotated texts from twelve text types. Annotations include:
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AMALGUM is a machine annotated multilayer corpus following the same design and annotation layers as GUM, but substantially larger (around 4M tokens). The goal of this corpus is to close the gap between high quality, richly annotated, but small datasets, and the larger but shallowly annotated corpora that are often scraped from the Web.
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The Alexa Point of View dataset is point of view conversion dataset, a parallel corpus of messages spoken to a virtual assistant and the converted messages for delivery. The dataset contains parallel corpus of input (input column) message and POV converted messages (output column). An example of a pair is tell @CN@ that i'll be late [\t] hi @CN@, @SCN@ would like you to know that they'll be late. The input and pov-converted output pair is tab separated. @CN@ tag is a placeholder for the contact name (receiver) and @SCN@ tag is a placeholder for source contact name (sender). The total dataset has 46563 pairs. This data is then test/train/dev split into 6985 pairs/32594 pairs/6985 pairs.
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IgboNLP is a standard machine translation benchmark dataset for Igbo. It consists of 10,000 English-Igbo human-level quality sentence pairs mostly from the news domain.
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The Manually Annotated Sub-Corpus (MASC) consists of approximately 500,000 words of contemporary American English written and spoken data drawn from the Open American National Corpus (OANC).
PTB-tagged English Tweets
Ensemble Tagger Training and Testing Set This data includes two files: The training set used to create the SCANL Ensemble tagger [1] and the "unseen" testing set that includes words from systems that are not available in the training set. These are derived from a prior dataset of Grammar Patterns; described in a different paper [2]. Within each of these csv files, you'll find several columns. We explain these columns below:
The data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset. The tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged jointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs are completely compatible over a whole tweet, is that tweet added to the dataset.