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92 results for “pos”
SPACCC_POS
<p>[PlanTL/medicine/annotated corpus/guidelines/Part-of-Speech] First version of the Part-of-Speech annotations in the Spanish Clinical Case Corpus that have been carried out by means of the Spanish Clinical Case Corpus Part-of-Speech Tagger based on FreeLing3.1 (SPACCC_POS-TAGGER, <a href="https://github.com/PlanTL/SPACCC_POS-TAGGER">https://github.com/PlanTL/SPACCC_POS-TAGGER</a>).</p> <p>Copyright (c) 2018 Secretaría de Estado para el Avance Digital</p>
A part-of-speech (POS) tagged corpus of Classical Tibetan
<p>This part-of-speech (POS) tagged corpus of Classical Tibetan was prepared in the course of the research project 'Tibetan in Digital Communication' (2012-2015) hosted at SOAS, University of London and funded by the UK's Arts and Humanities Research Council (grant code: AH/J00152X/1). For a description of the tag set see Garrett et al. 2014. and Garrett et al. 2015. This corpus includes the <em>Mdzaṅs blun</em> (9th century, canonical), the <em>Bu ston chos ḥbyuṅ</em> (13th century, ecclesiastical history), the <em>Mi la ras paḥi rnam thar</em> and <em>Mar paḥi rnam thar</em> (15th century, biography).</p>
A rule based Tibetan part-of-speech (POS) tagger for the creation of gold standard training data
<p>This rule based Tibetan part-of-speech (POS) tagger was prepared in the course of the research project 'Tibetan in Digital Communication' (2012-2015) hosted at SOAS, University of London and funded by the UK's Arts and Humanities Research Council (grant code: AH/J00152X/1). For a description of the tag set see Garrett et al. 2014. and Garrett et al. 2015. For a description of the tagger itself see Garrett et al. 2014. Note that the tagger must be used together with a lexicon (for example Hill & Garrett 2017a). One must use one's own script to tag all words with all tags in the lexicon and then apply the tagger to remove incorrect tags.</p> <p>On the associated corpus of 318,230 words (Hill & Garrett 2017b) the lexical tagger (i.e. simply applying all available tags to all words) tags 141,911 words with the correct unique tag, achieves as accuracy of 1.000 (by definition getting the right tag among others for each word) with an ambiguity of 2.73111. In contrast, the Rule Tagger tags 241,256 words with the correct unique tag, achieves an accuracy of 0.99893 and an ambiguity of 1.38577.</p> <p>Because this tagger does not achieve ambiguity 1.000 it is not suitable for tagging large scale corpora, but instead is useful for the creation of gold standard training data.</p> <p>N.B. In some rare cases the tagger removes all POS-tags.</p>
Pennsylvania German word list (lemmatized and POS-annotated)
<p>The file presents the words used in the Pennsylvania German part of the ENDE corpus (www.deitsch.eu). The list contains every lemma with its associated word forms documented in the corpus, comprised of 1761 lemmata and 2704 word forms.</p> <p>The ENDE corpus (“English-Deitsch translation corpus”) is the first POS-annotated and searchable text corpus in Pennsylvania German (= Deitsch; ISO language code: pdc), aligned to the English source texts. Despite many digital texts in Deitsch are available on the internet, there are, so far, no digital corpora for this language. This is due mainly to the lack of a generally recognized standard variety which could serve as a reference point for the linguistic analysis needed for lemmatization and annotation.</p> <p>Lemmatization was done with the help of different lexicographic resources (https://www.deitsch.eu/news/view/9) most of which follow other spelling conventions. A fair number of word forms, especially English loanwords of some sort, cannot be found in the dictionaries. Moreover, the variety used here is characterized by a high variability regarding not only the spelling but also other aspects of the language.</p> <p>Part-of-speech tags were assigned manually (see tagsets A and B below). These tagsets for part-of-speech annotation of Deitsch texts are based on the 2017 version of the STTS system created and widely used for German (https://ids-pub.bsz-bw.de/frontdoor/deliver/index/docId/6063/file/Westpfahl_Schmidt_Jonietz_Borlinghaus_STTS_2_0_2017.pdf), which has been slightly modified and adapted to the corpus texts written in the Plain Deitsch variety. Tagset A gives a broader view and refers to the lemma level, tagset B is more fine-grained and suitable for the single word forms documented in the corpus. Only those tags are listed which are actually employed for the annotation of the corpus texts. Foreign items not integrated in the Deitsch text flow (e.g. English quotations) have been omitted.</p> <p>For more details about the corpus and the project please refer to the above mentioned website.</p>
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone. in Palaeohistology helps reveal taxonomic variability in exceptionally large temnospondyl humeri from the Upper Triassic of Krasiejów, SW Poland
parallel-fibered bone; A5, osteocyte lacunae with well-preserved canaliculi; B3, osteocyte lacunae lacking canaliculi; B4, B5, growth pattern with preserved residuals of the thick annuli and zones (zo I–III) and thin annuli and zones (zo IV–VII); A6, growth pattern with preserved thin annuli and thick zones (zo I–IV), the dotted line marks the border between the perimedullary region and the cortex. Arrows in A5 and B3 indicate osteocyte lacunae; in B4, B5, and A6 indicate the annuli. Growth pattern in B4 figured on the lateral section side, in B5 and A5 on the ventral side; note the cortex thickness variation between B4 and B5. A1, A3, A4, A6, B1, B4, B5 in polarized light and A2, A5, B2, B3 in normal transmitted light. Abbreviations: an, annulus; ec, erosion cavity; pmr, perimedullary region; pos, primary osteon; sos, secondary osteon; zo, zone.
Specialised POS Tagged Syriac Corpus for State Morphology
<h1>Overview</h1> <p>A total of twelve .TXT files each representing a Syriac text that has been transcribed and tagged for part-of-speech (POS). This corpus forms part of a PhD research project on the historical syntax of Aramaic (Syriac) at The Australian National University (2020—current) in Canberra, Australia. This research project is interested in noun state morphology, among other topics, which is reflected in the POS scheme for this corpus.</p> <h2>Method</h2> <p>A detailed summary of this methodology is provided in El-Khaissi (data paper in review with the <em>Journal of Open Data Humanities</em>).</p> <ul> <li>Transcriptions are sourced from <a href="https://syriaccorpus.org/" target="_blank" rel="noopener">Digital Syriac Corpus.</a></li> <li>POS tags are based on word matches using <a href="https://sedra.bethmardutho.org/about/openapi" target="_blank" rel="noopener">SEDRA IV API (v1.0.0).</a></li> <li>Selection of Syriac texts was optimised to minimise external influence on Syriac grammar and maximise full coverage of key periods of the Syriac language from 2nd—13th century AD.</li> </ul> <h2>POS Format & Abbreviations </h2> <p>POS tags in the text files follow the following format:</p> <blockquote> <p><syntax-category>-<state>_<syriac_word></p> </blockquote> <p>Thus, an underscore '_' marks the beginning of a tag sequence while tag values are separated by hyphen(s) '-'. For example (noting text directionality constraints):</p> <blockquote> <pre>ܒܘܪܟܬܐ_EMP-N</pre> </blockquote> <p>The following abbreviation lists the definition of all POS tags, which are based on the parameters available in <a href="https://sedra.bethmardutho.org/about/openapi" target="_blank" rel="noopener">SEDRA IV API (v1.0.0).</a></p> <table> <tbody> <tr> <td>Absolute state noun (indeterminate relic)</td> <td>ABS</td> </tr> <tr> <td>Emphatic state noun (new indeterminate)</td> <td>EMP</td> </tr> <tr> <td>Construct state noun (bound noun)</td> <td>CNS</td> </tr> <tr> <td>State not applicable</td> <td>X</td> </tr> <tr> <td>particle</td> <td>PTCL</td> </tr> <tr> <td>pronoun</td> <td>PRO</td> </tr> <tr> <td>preposition</td> <td>PREP</td> </tr> <tr> <td>verb</td> <td>V</td> </tr> <tr> <td>denominative</td> <td>DEN</td> </tr> <tr> <td>noun</td> <td>N</td> </tr> <tr> <td>numeral</td> <td>NUM</td> </tr> <tr> <td>substantive</td> <td>SBV</td> </tr> <tr> <td>adjective</td> <td>ADJ</td> </tr> <tr> <td>proper noun</td> <td>PN</td> </tr> <tr> <td>adverb</td> <td>ADV</td> </tr> <tr> <td>demonym</td> <td>DNM</td> </tr> <tr> <td>participle adjective</td> <td>PTCPADJ</td> </tr> <tr> <td>adverb</td> <td>ADV</td> </tr> <tr> <td>idiom</td> <td>IDM</td> </tr> <tr> <td>See Quality Control & Limitations below</td> <td>DUP</td> </tr> </tbody> </table> <h2>Quality Control & Limitations</h2> <p>On average per manuscript, the POS-tagging process achieved a 63.13% saturation of texts. The POS tagging process was based on an exact-match process, which does not take into account syntactic or semantic context. Syriac words which exhibit homonymy are thus tagged with the value 'DUP' and should be assessed manually based on its original context. Among all 297,981 words in the corpus with an available POS tag, approximately 73,188 (24.56%) of tags reflected some kind of homonymy involving a word with various semantic and/or syntactic interpretations.</p> <p>Since this dataset was created as part of a research project investigating noun state morphology, additional tags were created targetting various state values. Grammatical elements, like number and gender, were not required as part of this investigation and therefore excluded from the POS-tagging process.</p> <h2>Contact</h2> <p>For any questions, please contact Charbel El-Khaissi <Charbel.El-Khaissi@anu.edu.au>.</p>
An unambiguous POS tagging set
<p>This data set contains 1,123 short, POS-tagged sentences (extracted from an earlier data set; see https://zenodo.org/records/7694423), using the Universal tag set. The sentences can easily be POS tagged by a human tagger. However, standard POS taggers struggle with these sentences. The data file contains a header row that describes each column. The first column indicates the type of sentence (either a transcript of spoken text (0) or a sentence originating from written text (1)), the second column contains the actual sentence, with ground truth POS tags. The third column indicates the index of the mistagged token, and the remaining five columns show the tags assigned (of which at least one is a mistagging) of five different taggers.</p>
Lab_13_OTHER_pos - Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards
<h1>Lab_13_OTHER_pos - Concordant inter-laboratory derived concentrations of ceramides in human plasma reference materials via authentic standards - mzML files</h1><p>This dataset is part of the <a href="https://doi.org/10.5281/zenodo.12632989" target="_blank">ILS Ceramide Ring Trial</a>. The suffix 'SOP' indicates that the results were obtained using the recommended and standard operating procedure protocol to prepare and measure all samples, while the suffix 'OTHER' indicates that the corresponding lab prepared and measured the samples according to their own internal protocol. Please check the corresponding mapping file 'ILS-Ceramide-Ring-Trial-Datasets.csv' in the ILS Ceramide Ring Trial record for a mapping of the originally submitted lab reports and the final lab number as reported in the manuscript.</p><p>All reports together with the code for analysis and visualization, reproducing the figures in the manuscript, are available under the following doi: <a href="https://zenodo.org/doi/10.5281/zenodo.10081970" target="_blank">https://zenodo.org/doi/10.5281/zenodo.10081970</a>. This links to releases of the following GitHub repository: <a href="https://github.com/lifs-tools/ils-ceramide-ring-trial" target="_blank">https://github.com/lifs-tools/ils-ceramide-ring-trial</a>. The archived version of the lab reports, workflow source code and manuscript visualizations are also available <a href="https://zenodo.org/doi/10.5281/zenodo.10081970" target="_blank">here</a>.</p><p>Please note that most datasets have been acquired in MRM mode, such that the msconvert conversion to mzML has stored the MRM data in the chromatogram part of the mzML files.</p> <p>The msconvert Docker container (Proteowizard release: 3.0.24172 (63d00b1), build date Jun 202 2024 20:01:14) was used with the native vendor libraries / peak picking for conversion, using default arguments. m/z values were encoded with 64 bit (default), while intensity values were encoded with 32 bit (default). All binary data was zlib-compressed.</p>
A part-of-speech (POS) lexicon of Classical Tibetan for NLP
<p>This part-of-speech (POS) lexicon of Classical Tibetan was prepared in the course of the research project 'Tibetan in Digital Communication' (2012-2015) hosted at SOAS, University of London and funded by the UK's Arts and Humanities Research Council (grant code: AH/J00152X/1). The data for verbs comes from a digitized version of <em>A Lexicon of Tibetan Verb Stems as Reported by the Grammatical Tradition</em> (Munich: Bayerische Akademie der Wissenschaften, 2010) by Nathan W. Hill. Otherwise data comes from the manually part-of-speech tagged training data produced by the corpus and a few lexical items specifically added by hand to improve rule based tagging.</p>
The Annotated Corpus of Classical Tibetan (ACTib), Part II - POS-tagged version, based on the BDRC digitised text collection, tagged with the Memory-Based Tagger from TiMBL
<p>This corpus is a part-of-speech tagged version of</p> <p>Wallman, Jeff, Rowinski, Zach, Ngawang Trinley, Tomlinson, Chris, & Keutzer, Kurt. (2017). Collection of Tibetan etexts compiled by the Buddhist Digital Resource Center [Data set]. Zenodo. http://doi.org/10.5281/zenodo.821218</p> <p>using the training data of</p> <p>Hill, Nathan W., & Garrett, Edward. (2017). A part-of-speech (POS) tagged corpus of Classical Tibetan [Data set]. Zenodo. http://doi.org/10.5281/zenodo.574878</p> <p>Please note that the files are not post-processed or manually corrected and that a small number of files in the KarmaDelek directory were still annotated, although the original xml-input was corrupted already.</p> <p> </p> <p>using the memory based tagger of</p> <p>https://languagemachines.github.io/mbt/</p>
MSV000085119_pos
<p>ENPKG ready dataset, build from the files in positive ionization mode of https://doi.org/doi:10.25345/C5S401</p>
A Study to Evaluate the Efficacy and Safety of Pertuzumab + Trastuzumab + Docetaxel Versus Placebo + Trastuzumab + Docetaxel in Previously Untreated Human Epidermal Growth Factor Receptor 2 (HER2)-Pos
ClinicalTrials.gov study NCT02896855. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Hypernym-LIBre: A free Web-based Corpus for Hypernym Detection [PoS and Dep-parsed Version]
<p>Hypernym-LIBre ( DOI: 10.5281/zenodo.3662204 ) is a free Web-based corpus for Hypernym detection.</p> <p>Here we provide its part-of-speech tagged and dependency-parsed version. Both have been annotated in the text as follows:</p> <p>For every word in the corpus, there is a PoS tag and dependency annotation separated by a '-'.</p> <p>Example: corrected-VERB-ROOT where corrected is the raw word followed by its PoS and dependency annotation.</p> <p> </p> <p>The file is provided as a zip file:</p> <p>Compressed File Size: ~15GB</p> <p>Total Size: 80.6GB uncompressed</p> <p>Number of Files: 442 files, ~180MB each</p> <p> </p> <pre>10.5281/zenodo.3695237</pre> <p>hyponym-hypernym pairs extracted from Hypernym-LIBre using Hearst patterns</p>
MSV000086161_pos
<p>ENPKG ready dataset, build from the files in positive ionization mode of https://doi.org/doi:10.25345/C5SB50</p><p> </p>
Gas Fees and Unconfirmed Transactions in Ethereum: A Proof-of-Stake (PoS) Focus}
Open the record for dataset details and reuse information.
POS-PC1
<p>Cleaned, anonymized data supporting, "The surprising power of a click requirement: How click requirements and warnings affect users' willingness to disclose personal information."</p>
untargeted metabolomics for IFN-γ treated BMECs, pos mode
<p>all the metabolites identified by untargeted metabolomics for IFN-γ treated BMECs in positive ion mode</p>
Subspecies and Distribution. pos sagitta Pallas, 1773 — S Russia (Altai Krai) and right bank of Irtysh River in Kazakhstan (Pavlodar and East Kazakhstan regions). D.s.aksuensisWangSung,1964—NWChina(NTarimBasininXinjiang). D.s.austrouralensisShenbrot,1991—NWKazakhstan(WestKazakhstan,Atyrau,andAktoberegionsbetweenUralandEmbarivers). D.s.bulganensisShenbrot,1991—EKazakhstan(ELakeZaysaninEastKazakhstanRegion),NWChina(DzungarianBasininXinjiang),andMongolia(SKhovdandSWGovi-Altai). D.s.deasyiBarret-Hamilton,1900—NWChina(STarimBasininXinjiangandS&WQaidamBasininQinghai). D. s. fuscocanus Wang Sung, 1964 — W China (S foothills of E Tian Shan in Xinjiang). D. s. halli Sowerby, 1920 — China (NE Inner Mongolia [= Nei Mongol], SW Heilongjiang, NWJilin, and N Liaoning) and SE Mongolia (Stikhbaatar). D. s. innae Ognev, 1930 — S Russia (Astrakhan Region E of Volga River) and NW Kazakhstan (West Kazakhstan and Atyrau regions W of Ural River). D. s. lagopus Lichtenstein, 1823 — WC Kazakhstan (E of Emba and N of Syrdarya rivers). D. s. megacranius Shenbrot, 1991 — SE Kazakhstan (Moinkum Sands in Jambyl Region). D. s. nogai Satunin, 1907 — S European Russia (Volgograd and Astrakhan regions E of Volga River, Kalmykia, and Dagestan). D. s. sowerbyi Thomas, 1908 — N China (NE Xinjiang, N Qaidam Basin in Qinghai, Gansu, SW Inner Mongolia, N Ningxia, and N Shaanxi) and Mongolia. D. s. turanicus Shenbrot, 1991 — SW Kazakhstan (S Kyzylorda S of Syrdarya River and Mangystau regions), Uzbekistan, and Turkmenistan; it probably occurs in adjacent W Afghanistan. D. s. ubsanensis Bannikov, 1947 — NW Mongolia (N Uvs) and adjacent Russia (extreme S Tuva). D. s. usuni Shenbrot, 1991 — SE Kazakhstan (Almaty Region); it probably occurs in adjacent China (sands of Ili Valley of W Xinjiang). D. s. zaissanensis Selevin, 1934 — E Kazakhstan (NW Lake Zaysan Basin on the left bank of Irtysh River). Isolated population in N Iran (Turan Desert in E Semnan Province) may belong to turanicus or correspond to a yet undescribed subspecies. in Dipodidae
Subspecies and Distribution. pos sagitta Pallas, 1773 — S Russia (Altai Krai) and right bank of Irtysh River in Kazakhstan (Pavlodar and East Kazakhstan regions). D.s.aksuensisWangSung,1964—NWChina(NTarimBasininXinjiang). D.s.austrouralensisShenbrot,1991—NWKazakhstan(WestKazakhstan,Atyrau,andAktoberegionsbetweenUralandEmbarivers). D.s.bulganensisShenbrot,1991—EKazakhstan(ELakeZaysaninEastKazakhstanRegion),NWChina(DzungarianBasininXinjiang),andMongolia(SKhovdandSWGovi-Altai). D.s.deasyiBarret-Hamilton,1900—NWChina(STarimBasininXinjiangandS&WQaidamBasininQinghai). D. s. fuscocanus Wang Sung, 1964 — W China (S foothills of E Tian Shan in Xinjiang). D. s. halli Sowerby, 1920 — China (NE Inner Mongolia [= Nei Mongol], SW Heilongjiang, NWJilin, and N Liaoning) and SE Mongolia (Stikhbaatar). D. s. innae Ognev, 1930 — S Russia (Astrakhan Region E of Volga River) and NW Kazakhstan (West Kazakhstan and Atyrau regions W of Ural River). D. s. lagopus Lichtenstein, 1823 — WC Kazakhstan (E of Emba and N of Syrdarya rivers). D. s. megacranius Shenbrot, 1991 — SE Kazakhstan (Moinkum Sands in Jambyl Region). D. s. nogai Satunin, 1907 — S European Russia (Volgograd and Astrakhan regions E of Volga River, Kalmykia, and Dagestan). D. s. sowerbyi Thomas, 1908 — N China (NE Xinjiang, N Qaidam Basin in Qinghai, Gansu, SW Inner Mongolia, N Ningxia, and N Shaanxi) and Mongolia. D. s. turanicus Shenbrot, 1991 — SW Kazakhstan (S Kyzylorda S of Syrdarya River and Mangystau regions), Uzbekistan, and Turkmenistan; it probably occurs in adjacent W Afghanistan. D. s. ubsanensis Bannikov, 1947 — NW Mongolia (N Uvs) and adjacent Russia (extreme S Tuva). D. s. usuni Shenbrot, 1991 — SE Kazakhstan (Almaty Region); it probably occurs in adjacent China (sands of Ili Valley of W Xinjiang). D. s. zaissanensis Selevin, 1934 — E Kazakhstan (NW Lake Zaysan Basin on the left bank of Irtysh River). Isolated population in N Iran (Turan Desert in E Semnan Province) may belong to turanicus or correspond to a yet undescribed subspecies.
POS-OC
<p>Cleaned, anonymized data supporting, "How a daily regimen of operant conditioning might explain the power of the Search Engine Manipulation Effect (SEME)."</p>
POS-VME Experiments 1 & 2
<p>Cleaned, anonymized data supporting, "The Video Manipulation Effect (VME): A Quantification of the Possible Impact that the Ordering of YouTube Videos Might Have on Opinions and Voting Preferences."</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.