Constituency Parsing

The English Penn Treebank (PTB) corpus, and in particular the section of the corpus corresponding to the articles of Wall Street Journal (WSJ), is one of the most known and used corpus for the evaluation of models for sequence labelling. The task consists of annotating each word with its Part-of-Speech tag. In the most common split of this corpus, sections from 0 to 18 are used for training (38 219 sentences, 912 344 tokens), sections from 19 to 21 are used for validation (5 527 sentences, 131 768 tokens), and sections from 22 to 24 are used for testing (5 462 sentences, 129 654 tokens). The corpus is also commonly used for character-level and word-level Language Modelling. Source: Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling Image Source: https://dl.acm.org/doi/10.5555/972470.972475

The ECGs in this collection were obtained using a non-commercial, PTB prototype recorder with the following specifications: 16 input channels, (14 for ECGs, 1 for respiration, 1 for line voltage) Input voltage: ±16 mV, compensated offset voltage up to ± 300 mV Input resistance: 100 Ω (DC) Resolution: 16 bit with 0.5 μV/LSB (2000 A/D units per mV) Bandwidth: 0 - 1 kHz (synchronous sampling of all channels) Noise voltage: max. 10 μV (pp), respectively 3 μV (RMS) with input short circuit Online recording of skin resistance Noise level recording during signal collection The database contains 549 records from 290 subjects (aged 17 to 87, mean 57.2; 209 men, mean age 55.5, and 81 women, mean age 61.6; ages were not recorded for 1 female and 14 male subjects). Each subject is represented by one to five records. There are no subjects numbered 124, 132, 134, or 161. Each record includes 15 simultaneously measured signals: the conventional 12 leads (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6) together with the 3 Frank lead ECGs (vx, vy, vz). Each signal is digitized at 1000 samples per second, with 16 bit resolution over a range of ± 16.384 mV. On special request to the contributors of the database, recordings may be available at sampling rates up to 10 KHz. Within the header (.hea) file of most of these ECG records is a detailed clinical summary, including age, gender, diagnosis, and where applicable, data on medical history, medication and interventions, coronary artery pathology, ventriculography, echocardiography, and hemodynamics. The clinical summary is not available for 22 subjects. Source: PTB

FLUE is a French Language Understanding Evaluation benchmark. It consists of 5 tasks: Text Classification, Paraphrasing, Natural Language Inference, Constituency Parsing and Part-of-Speech Tagging, and Word Sense Disambiguation.

Taiga is a corpus, where text sources and their meta-information are collected according to popular ML tasks. Each text in corpus is represented in plain text and with morphological and syntactic annotation (UDPipe, homonymy resolved automatically) + has metainformation - date, theme, authorship, text difficulcy…etc (depending on source) By now, about 5 billions of words are 77% literary texts (33 literary magazines), 19% of naive poetry, 2% of news (4 popular sites) and 2% of other (popular science, culture mags, social networks, amateur poems and prose), with documentation available. Segments Info | Genres | Tokens, millions | % | |------------------|------------------|-----| | News | 92 | 1.5 | | Literary Texts | 4605 | 76 | | Special datasets | 2.5 | 0.5 | | Social media | 80 | 1.5 | | Subtitles | 101 | 1.5 | | Poems | 1130 | 19 | **Annotation Example ** (CONLL-u): # newdoc # newpar # sent_id = 1 # 2003Armeniya.xml 1 # text = В советский период времени число ИТ- специалистов в Армении составляло около десяти тысяч. # sent_id = 1 1 В в ADP _ _ 3 case 3:case _ 2 советский советский ADJ _ Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|Number=Sing 3 amod 3:amod _ 3 период период NOUN _ Animacy=Inan|Case=Acc|Gender=Masc|Number=Sing 11 obl 11:obl _ 4 времени время NOUN _ Animacy=Inan|Case=Gen|Gender=Neut|Number=Sing 3 nmod 3:nmod _ 5 число число NOUN _ Animacy=Inan|Case=Acc|Gender=Neut|Number=Sing 11 obj 11:obj _ 6 ИТ ит PROPN _ Animacy=Inan|Case=Nom|Gender=Neut|Number=Sing 8 compound 8:compound SpaceAfter=No 7 - - PUNCT _ _ 6 punct 6:punct _ 8 специалистов специалист NOUN _ Animacy=Anim|Case=Gen|Gender=Masc|Number=Plur 5 nmod 5:nmod _ 9 в в ADP _ _ 10 case 10:case _ 10 Армении армения PROPN _ Animacy=Inan|Case=Loc|Gender=Fem|Number=Sing 5 nmod 5:nmod _ 11 составляло составлять VERB _ Aspect=Imp|Gender=Neut|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act 0 root 0:root _ 12 около около ADP _ _ 14 case 14:case _ 13 десяти десять NUM _ Case=Gen 14 nummod 14:nummod _ 14 тысяч тысяча NOUN _ Animacy=Inan|Case=Gen|Gender=Fem|Number=Plur 11 nsubj 11:nsubj SpaceAfter=No 15 . . PUNCT _ _ 14 punct 14:punct _

The Potsdam Commentary Corpus (PCC) is a corpus of 220 German newspaper commentaries (2.900 sentences, 44.000 tokens) taken from the online issues of the Märkische Allgemeine Zeitung (MAZ subcorpus) and Tagesspiegel (ProCon subcorpus) and is annotated with a range of different types of linguistic information. The central subcorpus that we are making publicly available consists of 176 MAZ texts, which are annotated with * Sentence Syntax * Coreference * Discourse Structure (RST & PDTB) * Aboutness topics

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. Source: https://github.com/alexa/alexa-point-of-view-dataset

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). All of MASC includes manually validated annotations for sentence boundaries, token, lemma and POS; noun and verb chunks; named entities (person, location, organization, date); Penn Treebank syntax; coreference; and discourse structure. Additional manually produced or validated annotations have been produced by the MASC project for portions of the sub-corpus, including full-text annotation for FrameNet frame elements and a 100K+ sentence corpus with WordNet 3.1 sense tags, of which one-tenth are also annotated for FrameNet frame elements. Annotations of all or portions of the sub-corpus for a wide variety of other linguistic phenomena have been contributed by other projects, including PropBank, TimeBank, Pittsburgh opinion, and several others. Unlike most freely available corpora including a wide variety of linguistic annotations, MASC contains a balanced selection of texts from a broad range of genres.