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9 results for “Semantic tagging”
SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information
<p>The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular).</p> <p>The dataset consists of 777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation.</p> <p>The structure of the data is as follows:<br> 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks.<br> 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials.<br> Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type.</p> <p>1-Road-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>1-Road-Mask</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p>2-NoRoad-Ortho</p> <p> |----Train</p> <p> |----Test</p> <p> -----Validation</p> <p> </p>
Semantically tagged Finnish Wikipedia 2017
<p><strong>Description of FI Wikipedia 2017 tagging</strong></p> <p><strong>Kimmo Kettunen</strong></p> <p><strong>University of Eastern Finl</strong><strong>and</strong></p> <p>The tagged data contains the texts of the Finnish Wikipedia of 2017. It has been first tagged syntactically in the Language Bank of Finland using the available UD2 tagger version of the Mylly service (https://mylly.rahtiapp.fi/home).</p> <p>Semantic tags to the UD2 parse have been added using a lexical semantic tagger FiST (Kettunen, 2019, <a href="https://aclanthology.org/W19-0306/">https://aclanthology.org/W19-0306/</a>).</p> <p>This published version has been condensed to a format where each analysed word contains the</p> <p>1. original running word form,</p> <p>2. lemma of the word form from UD2 parse,</p> <p>3. part-of-speech of the word from FiST</p> <p>4. semantic tag(s) for the word from FiST, and</p> <p>5. syntactic function of the word from UD2 parse.</p> <p>Semantic tags used are explained in this UCREL Semantic Analysis System (USAS) document: <a href="https://ucrel.lancs.ac.uk/usas/USASSemanticTagset.pdf">https://ucrel.lancs.ac.uk/usas/USASSemanticTagset.pdf</a></p> <p>Tagging includes all the semantic tags available for the word, as FiST does not perform disambiguation. Unknown words for the tagger are marked with tag Z99. Punctuation is tagged with PUNCT and numbers with NUMB. Lines beginning with # are output of UD2 and contain document, paragraph and sentence information.</p> <p>The output contains 6 415 027 sentences and 98.81 million lines. Lexical coverage of the semantic tagging is 76.59 %</p> <p><strong>Examples of output</strong></p> <p># newdoc</p> <p># newpar</p> <p># sent_id = 1</p> <p># text = Amsterdam</p> <p>Amsterdam#Amsterdam#Proper#Z2 root</p> <p># newpar</p> <p># sent_id = 2</p> <p># text = Amsterdam on Alankomaiden pääkaupunki.</p> <p>Amsterdam#Amsterdam#Proper#Z2 nsubj:cop</p> <p>on#olla#Verb#A3+ A1.1.1 M6 Z5 cop</p> <p>Alankomaiden#Alankomaat#Proper#Z2 nmod:poss</p> <p>pääkaupunki#pääkaupunki#Noun#M7 root</p> <p>. PUNCT</p>
Semantically tagged Finnish parliament discussions 1991-2015
<p><strong>Semantically tagged Finnish parliament discussions 1991-2015</strong></p> <p>The original data is this (Rauh et al, 2017):</p> <p>https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/E4RSP9</p> <p>We have imported the raw text out of the original data set without speaker and party tags. The Finnish text has been first tagged with UD2 parser using the Mylly service of the Language Bank of Finland. After UD2 parse, semantic tags have been added to the text with FiST (Kettunen, 2019).</p> <p><strong>Output form</strong></p> <p># newdoc</p> <p># newpar</p> <p># sent_id = 1</p> <p># text = Arvoisa herra puhemies!</p> <p>Arvoisa arvoisa Z99 amod</p> <p>herra#herra#Noun#S2.2m S9 compound:nn</p> <p>puhemies#puhemies#Noun#G1.1/S2 root</p> <p>! PUNCT</p> <p> </p> <p>The output consists of the 1) token word form of the text, 2) lemma of the token, 3) POS, 4) semantic tag and 5) UD2 grammatical relation of the word in the sentence.</p> <p>Our semantic tagger does not resolve ambiguity. If the lexeme has multiple possible semantic tags, they are all included in the output (e.g., herra#herra#Noun#<strong>S2.2m S9</strong> compound:nn). Slash notation in the semantic tags (e.g., puhemies#puhemies#Noun#<strong>G1.1/S2</strong>) indicates that the word can belong to two or more categories (Löfbeg, 2017). If the semantic category of the word is not recognized, the word is tagged with Z99.</p> <p>The data consists of 4 036 269 sentences and ca. 65.247 million words. Lexical coverage of FiST for the data is 86.05 %, i.e. 86% of the words are known for the tagger and marked with a semantic tag.</p> <p><strong>References</strong></p> <p>Kettunen, Kimmo (2019). FiST – towards a Free Semantic Tagger of Modern Standard Finnish. IWCLUL2019, http://aclweb.org/anthology/W19-0306</p> <p>Rauh, Christian; De Wilde, Pieter; Schwalbach, Jan, 2017, "Corp_Eduskundta.Rdata", The ParlSpeech data set: Annotated full-text vectors of 3.9 million plenary speeches in the key legislative chambers of seven European states, https://doi.org/10.7910/DVN/E4RSP9/U8VZHK, Harvard Dataverse, V1.</p> <p>Lofberg, L. (2017). Creating large semantic lexical resources for the Finnish language. [Doctoral Thesis, Lancaster University]. Lancaster University. https://doi.org/10.17635/lancaster/thesis/3</p> <p>UCREL Semantic Analysis System (USAS). https://ucrel.lancs.ac.uk/usas/</p>
THE PROBLEM OF SEMANTIC TAGGING OF PHILOSOPHICAL TERMS IN THE UZBEK LANGUAGE IN THE CORPUS
Open the record for dataset details and reuse information.
Semantic metadata annotation: tagging medline abstracts for enhanced information access
<p>The object of this study is to develop methods for automatically annotating the argumentative role of sentences in scientific abstracts. Working from Medline abstracts, we classified sentences into four major argumentative roles: objective, method, result, conclusion. The idea is that if the role of each sentence can be marked up, then this metadata can be used during information retrieval to seek for particular types of information such as novelty, conclusions, methodologies, aims/goals of a scientific piece of work.</p> <p> </p>
Figure 3 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 3 - Flowchart of an integrated, XML-based editorial, publishing and dissemination process applied in ZooKeys through the Pensoft Mark Up Tool (PMT).
Figure 2 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 2 - Four stages of an XML-based editorial, publication and dissemination workflow applied in ZooKeys (stages 1, 2, 4) and/or Plazi (stages 3, 4). Forms in blue are either implemented or prototyped, forms in red are in a process of development.
Figure 4 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 4 - Pensoft Taxon Profile created dynamically by PMT and available through a link to any taxon name mentioned within a paper. In this case, this is the oak species Quercus suber L., cited in a zoological paper (Stoev et al. 2010). The red arrow indicates the "Create your own taxon profile" option, that may be used by the reader to create profiles of any taxon name or to improve search results for taxonomic names cited in the paper.
Figure 1 from: Penev L, Agosti D, Georgiev T, Catapano T, Miller J, Blagoderov V, Roberts D, Smith V, Brake I, Ryrcroft S, Scott B, Johnson N, Sautter G, Chavan V, Robertson T, Remsen D, Stoev P, Parr C, Knapp S, Kress W, Thompson F, Erwin T (2010) Semantic tagging of and semantic enhancements to systematics papers: ZooKeys working examples. ZooKeys 50: 1-16. https://doi.org/10.3897/zookeys.50.538
Figure 1 - Conventional layout of a standard taxonomic publication in PDF format (A) and the same portion of text in XML-tagged format (B). Explanations: The sign "<" incidates the start tag and the symbol " denotes the start of the treatment and the tag (not visible here) marks up the end of the treatment within the text of the paper. The tags and denote the start and end of a particular section of the treatment, in this case the type material data (labelled as Type material.)
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