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85 results for “Lexicon”
iRead4Skills - Basic Lexicons per Complexity Level
<div> <p>The iRead4Skills Basic lexicons per Complexity Level consists of three basic lexicons per complexity level for French, Spanish, and Portuguese, provided in .xlsx format. These lexicons were compiled under the scope of the project iReadSkills – Intelligent Reading Improvement System for Fundamental and Transversal Skills Development, funded by the European Commission (grant number: 1010094837). The project aims to enhance reading skills within the adult population by creating an intelligent system that assesses text complexity and recommends suitable reading materials to adults with low literacy skills, contributing to reducing skills gaps and facilitating access to information and culture (https://iread4skills.com/).</p> </div> <div> <p>Each lexicon covers the complexity levels deemed relevant for the project - Very Easy (approximately A1), Easy (approximately A2), and Plain (approximately B1) -, and will contribute to the complexity analysis systems for the three languages of the project: French, Portuguese, and Spanish. The data files are accompanied by a description of the data. The baselines for each lexicon definition can be consulted here: iRead4Skills - Baselines for complexity lexicons definition (<a href="https://doi.org/10.5281/zenodo.10069793" target="_blank" rel="noreferrer noopener">https://doi.org/10.5281/zenodo.10069793</a>)</p> <p> </p> </div> <div> <p><strong>French lexicon</strong>: 10103 entries </p> </div> <div> <p><strong>Portuguese lexicon</strong>: 2 729 entries</p> </div> <div> <p><strong>Spanish lexicon</strong>: 3 033 entries </p> </div>
IT-VaLex: Index Thomisticus Valency Lexicon
<p>IT-VaLex: <em>Index Thomisticus</em> Valency Lexicon. Website: <a href="https://itreebank.marginalia.it/itvalex/">https://itreebank.marginalia.it/itvalex/</a></p>
Bootstrapped Lexicon of German Verbal Polarity Shifters
<p>We provide a bootstrapped lexicon of German verbal polarity shifters. Our lexicon covers 2595 verbs of GermaNet. Polarity shifter labels are given for each word lemma. All labels were assigned by an expert annotator who is a native speaker of German.</p> <p><strong>Data</strong></p> <p>The data consists of two lists of GermaNet verbs annotated for whether they cause shifting:</p> <ol> <li><code>verbal_shifters.gold_standard.txt</code>: The initial gold standard (§3) of 2000 randomly sampled verbs.</li> <li><code>verbal_shifters.bootstrapping.txt</code>: The bootstrapped 595 verbs (§5.3) that were labelled as shifters by our best classifier and then manually annotated.</li> </ol> <p><strong>Format</strong></p> <p>Each line contains a verb and its label, separate by a whitespace.</p> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Marc Schulder, Michael Wiegand, Josef Ruppenhofer (2018). <strong>"Automatically Creating a Lexicon of Verbal Polarity Shifters: Mono- and Cross-lingual Methods for German"</strong>. <em>Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018)</em>. Santa Fe, New Mexico, USA, August 20 - August 26, 2018. <a href="https://doi.org/10.5281/zenodo.3365694">DOI: 10.5281/zenodo.3365694</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p>
Lexicon of English Verbal Polarity Shifters
<p>We provide a complete lexicon of English verbal polarity shifters and their shifting scope. Our lexicon covers all verbs of WordNet v3.1 that are single word or particle verbs. Polarity shifter and scope labels are given for each lemma-synset pair (i.e. each word sense of a lemma).</p> <p><strong>Data</strong></p> <p>The data is presented in the following forms:</p> <ol> <li>A complete lexicon of all verbal shifters and their shifting scopes.</li> <li>Two auxiliary lists: <ol> <li>A list of all lemmas with shifter labels</li> <li>A list of all word senses with shifter labels</li> </ol> </li> </ol> <p>All files are in CSV (comma-separated value) format.</p> <p><strong>1. Main Lexicon</strong></p> <p>File name: <code>shifter_lexicon.csv</code></p> <p>The main lexicon lists all verbal shifters and their shifting scopes. Verbal shifters are modelled as lemma-sense pairs with one or more shifting scopes.</p> <p>Each line of the lexicon file contains a single lemma-sense-scope triple, using the format:</p> <pre><code>LEMMA,SYNSET,SCOPE </code></pre> <p>The elements are defined as follows:</p> <ul> <li><strong>LEMMA:</strong> The lemma form of the verb.</li> <li><strong>SYNSET:</strong> The numeric identifier of the synset, commonly referred to as <em>offset</em> or <em>database location</em>. It consists of 8 digits, including leading zeroes (e.g. 00334568).</li> <li><strong>SCOPE:</strong> The scope of the shifting: <ul> <li><code>subj</code>: The verbal shifter affects its subject.</li> <li><code>dobj</code>: The verbal shifter affects its direct object.</li> <li><code>pobj_*</code>: The verbal shifter affects objects within a prepositional phrase. The preposition in question is included in the annotation. For example a <em>from</em>-preposition scope receives the label <code>pobj_from</code> and a a <em>for</em>-preposition receives <code>pobj_for</code>.</li> <li><code>comp</code>: The verbal shifter affects a clausal complement, such as infinitive clauses or gerunds.</li> </ul> </li> </ul> <p>The lexicon lists all lemma-sense pairs that are verbal shifters. Any lemma-sense pair not listed is not a verbal shifter. When a lemma-sense pair has more than one possible scope, a separate entry is made for each scope.</p> <p><strong>2. Auxiliary Lists</strong></p> <p>The auxiliary files represent the same shifter information as the main lexicon, but for lemmas and synsets, respectively, instead of for lemma-sense pairs. Due to their nature, these lists are more coarse-grained than the main lexicon and contain no information on shifter scope. They are provided as a convenience for fast experimentation.</p> <p><strong>2.1. List of Lemmas</strong></p> <p>File name: <code>shifter_lemma_lexicon.csv</code></p> <p>List of all verb lemmas and whether they are shifters in at least one of their word senses.</p> <pre><code>LEMMA,LABEL </code></pre> <ul> <li><strong>LEMMA:</strong> The lemma form of the verb.</li> <li><strong>LABEL:</strong> <code>shifter</code> if the verb is a shifter in at least one of its word senses, otherwise <code>nonshifter</code>.</li> </ul> <p>Many verbal shifter lemmas only cause shifting in some of their word senses. This list is therefore considerably more coarse-grained than the main lexicon.</p> <p><strong>2.2. List of Synsets</strong></p> <p>File name: <code>shifter_synset_lexicon.csv</code></p> <p>List of all synsets and whether their lemmas are shifters in this specific word sense.</p> <pre><code>SYNSET,LABEL </code></pre> <ul> <li><strong>SYNSET:</strong> The numeric identifier of the synset, commonly referred to as <em>offset</em> or <em>database location</em>. It consists of 8 digits, including leading zeroes (e.g. 00334568).</li> <li><strong>LABEL:</strong> <code>shifter</code> if the word sense causes shifting, otherwise <code>nonshifter</code>.</li> </ul> <p>Shifting is shared among lemmas of the same word sense. This list, therefore, provides (almost) the same granularity for the shifter label as the main lexicon. However, in a few exceptions, synsets contained words with subtly different senses that did not all cause shifting. These senses are considered shifters in this list, analogous to the generalisation in the list of lemmas.</p> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Schulder, Marc and Wiegand, Michael and Ruppenhofer, Josef and Köser, Stephanie (2018). <strong>"Introducing a Lexicon of Verbal Polarity Shifters for English"</strong>. Proceedings of the 11th Conference on Language Resources and Evaluation (LREC). Miyazaki, Japan, May 7-12, 2018. <a href="https://doi.org/10.5281/zenodo.3365683">DOI: 10.5281/zenodo.3365683</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p>
Bootstrapped Lexicon of English Verbal Polarity Shifters
<blockquote> <p>An extended version of this dataset that also covers nominal and adjectival polarity shifters can be found at <a href="https://doi.org/10.5281/zenodo.3365601">doi:10.5281/zenodo.3365601</a>.</p> </blockquote> <p> </p> <p>We provide a bootstrapped lexicon of English verbal polarity shifters. Our lexicon covers 3043 verbs of WordNet v3.1 (Miller et al., 1990) that are single word or particle verbs. Polarity shifter labels are given for each word lemma.</p> <p><strong>Data</strong></p> <p>The data consists of:</p> <ol> <li>Two lists of WordNet verbs (Miller et al., 1990), annotated for whether they cause shifting. <ol> <li>The initial gold standard (§2) of 2000 randomly chosen verbs.</li> <li>The bootstrapped 1043 verbs (§5.3) that were labelled as shifters by our best classifier and then manually annotated.</li> </ol> </li> <li>Data set of verb phrases from the Amazon Product Review Data corpus (Jindal & Liu, 2008), annotated for polarity of phrase and polar noun.</li> </ol> <p> </p> <p><strong>1. Verbal Shifters</strong></p> <p><strong>Files</strong></p> <ul> <li>The initial gold standard: <code>verbal_shifters.gold_standard.txt</code></li> <li>The bootstrapped verbs: <code>verbal_shifters.bootstrapping.txt</code></li> </ul> <p><strong>Format</strong></p> <ul> <li>Each line contains a verb and its label, separate by a whitespace.</li> <li>Multiword expressions are separated by an underscore (WORD_WORD).</li> <li>All labels were assigned by an expert annotator.</li> </ul> <p> </p> <p><strong>2. Sentiment Verb Phrases</strong></p> <p><strong>Files</strong></p> <ul> <li>All annotated verb phrases: <code>sentiment_phrases.txt</code></li> </ul> <p><strong>Content</strong></p> <p>The file starts with 400 phrases containing shifter verbs, followed by 2231 phrases containing non-shifter verbs.</p> <p><strong>Format</strong></p> <p>Every item consists of:</p> <ul> <li>The sentence from which the VP and the polar noun were extracted.</li> <li>The VP, polar noun and the verb heading the VP.</li> <li>Constituency parse for the VP.</li> <li>Gold labels for VP and polar noun by a human annotator.</li> <li>Predicted labels for VP and polar noun by RNTN tagger (Socher et al., 2013) and <code>LEX_gold</code> approach.</li> <li>Items are separated by a line of asterisks (*)</li> </ul> <p><strong>Related Resources</strong></p> <ul> <li><strong>Paper:</strong> <a href="https://aclweb.org/anthology/I17-1063">ACL Anthology</a> or <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a></li> <li><strong>Presentation:</strong> <a href="https://www.aclweb.org/anthology/attachments/I17-1063.Presentation.pdf">ACL Anthology</a></li> <li><strong>Word Embedding:</strong> <a href="https://doi.org/10.5281/zenodo.3370051">DOI: 10.5281/zenodo.3370051</a></li> </ul> <p><strong>Attribution</strong></p> <p>This dataset was created as part of the following publication:</p> <p>Marc Schulder, Michael Wiegand, Josef Ruppenhofer and Benjamin Roth (2017). <strong>"Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features"</strong>. Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP). Taipei, Taiwan, November 27 - December 3, 2017. <a href="https://doi.org/10.5281/zenodo.3365609">DOI: 10.5281/zenodo.3365609</a>.</p> <p>If you use the data in your research or work, please cite the publication.</p> <p> </p>
Sentiment polarity lexicon of Bosnian language
<p>First sentiment annotated lexicon of the Bosnian language.</p> <p>The lexicon is divided into two files: positive and negative polarity.</p> <p>The lists are prepared in separate files, each file populated by words of the aforementioned polarity listed each word in a separate row. </p> <p>The positive polarity list BOSNIAN_POSITIVE.txt holds 1219 words.</p> <p>The negative polarity list BOSNIAN_NEGATIVE.txt holds 3935 words.</p>
Tiranige lexicon
<p>Lexicon of Tiranige, a western Dogon language of east central Mali, in csv spreadsheet form. Columns from left to right (omitting blanks) are:</p> <p>Tiranige (transcription)<br> finder (English finder list, i.e. usually one-word glosses)<br> recherche (finder list in French)<br> English (full English gloss)<br> français (full French gloss)<br> code (for certain lexical categories: fauna, flora, body, kinship)<br> order/family (for flora-fauna terms)<br> species (for flora-fauna terms)<br> synonymy (alternative or former species binomials)</p>
Communication score lexicon
<p>This lexicon was developed in the context of measuring <em>“openness of communication” in the paper named </em> ''Predicting Openness of Communication in Families with Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis''. To develop an <em>“openness of communication”</em> score we built a lexicon containing words and phrases linked to communication and we classified them as positive or negative.</p> <p>The lexicon contains 532 items (132 unigrams, 215 bigrams, 185 trigrams). Two people independently created the scoring of N-grams in the lexicon as positive or negative. More specifically, they evaluated each item on a 7-point scale on how favorable the items measure <em>“openness of communication”</em>. Scoring values ranged from -3 (extremely strong negative word related to communication) to +3 (extremely strong positive word related to communication).</p>
Lubrang Brokpa Lexicon - photographs
<p>These sound files constitute the elicitation of the lexical entries of the Basic Word List in the Lubrang variety of Brokpa. Lubrang village is a recent (early 20th century) settlement of Brokpa speaker originating in Sakteng village of Bhutan. They left Bhutan due to its heavy taxation of the semi-nomadic Brokpa households and its anti-Gelukpa policies and settled in the then Tibetan-administered area on land belonging to the Khispi people of Lish village, to which they continue to pay an annual tax. Lubrang Brokpa should hence be close to Merak and Sakteng (Bhutan) Brokpa, and not so close to Nyukmadung and Senge Brokpa spoken closer by. Because of the speaker’s paternal background there may be some admixture with Dirang Tshangla.</p>
Lubrang Brokpa Lexicon - sound files
<p>These sound files constitute the elicitation of the lexical entries of the Basic Word List in the Lubrang variety of Brokpa. Lubrang village is a recent (early 20<sup>th</sup> century) settlement of Brokpa speaker originating in Sakteng village of Bhutan. They left Bhutan due to its heavy taxation of the semi-nomadic Brokpa households and its anti-Gelukpa policies and settled in the then Tibetan-administered area on land belonging to the Khispi people of Lish village, to which they continue to pay an annual tax. Lubrang Brokpa should hence be close to Merak and Sakteng (Bhutan) Brokpa, and not so close to Nyukmadung and Senge Brokpa spoken closer by. Because of the speaker’s paternal background there may be some admixture with Dirang Tshangla.</p>
Loflòc: A Morphological Lexicon for Occitan using Universal Dependencies
<p><strong>LOFLOC -- Lexic obèrt flechit Occitan (Open Inflected Lexicon of Occitan)</strong></p> <p>Loflòc is a morphological lexicon for Occitan, a Romance language spoken in the south of France and in parts of Italy and Spain. Occitan is not recognized as an official language in France and no standard variety is shared across the linguistic area. To the best of our knowledge, Loflòc is the first publicly available lexicon for Occitan. It contains 680 thousand entries for 57 thousand lemmas. Each entry contains an inflected form, its lemma and its part-of-speech tag according to the Universal Dependencies guidelines. Currently, the lexicon only contains the Lengadocian variety and the classical spelling norm. Nevertheless, it has been shown to be useful even for processing texts from other varieties (for more details, see Vergez-Couret et al., 2024; full reference below).</p>
Lexicon of Place Names in the Alsatian Dialects
<p>This dataset contains a lexicon of place names in the Alsatian dialects. These place names were collected from several resources and manually categorised according to location types defined in the QUAERO project:</p> <ul> <li>loc.fac – Facility</li> <li>loc.phys.astro – Astronym</li> <li>loc.phys.geo – Geonym</li> <li>loc.phys.hydro – Hydronym</li> <li>loc.adm.nat – Country</li> <li>loc.adm.reg – Region</li> <li>loc.adm.sup – Supranational</li> <li>loc.adm.town – City</li> <li>loc.oro – Odonym</li> </ul> <p>The CSV file contains 4 columns:<br> 1. Place name in Alsatian<br> 2. Place name in French<br> 3. Quaero category<br> 4. Source(s): WikiAls (articles from the Alemannic Wikipedia), WikiFr (articles from the French Wikipedia), corpus (Wikipedia articles from the Alemannic Wikipedia and chronicles from an information magazine published by the Haut-Rhin department (southern Alsace) General Council). This field also indicates whether the same spelling can be found in other lexicons of place names for Alsatian: AlsaDico (Edmond Jung. <em>L’alsadico : 22 000 mots et expressions français-alsacien</em>. La<br> Nuée bleue, Strasbourg, 2006.) and Elsàsser (Marc Hug. <em>Toponymes d’Alsace</em>. Online, <a href="http://elsasser.free.fr/NomCommu/ecrantot.html">http://elsasser.free.fr/<br> NomCommu/ecrantot.html</a>, 2007.)</p> <p>The dataset was produced in the context of the RESTAURE project, funded by the French ANR. The lexicon is also decribed in the following article: <a href="https://hal.archives-ouvertes.fr/hal-01702656">https://hal.archives-ouvertes.fr/hal-01702656</a>.</p>
RedMed: Extending drug lexicons for social media applications
<p>Data associated with the RedMed project.</p> <p>Details for the process behind the data creation can be found in the associated paper:</p> <p><strong>Lavertu, A. & Altman, R. B. </strong>"RedMed: Extending drug lexicons for social media applications"<br> Journal of Biomedical Informatics, (2019)</p> <p><a href="https://doi.org/10.1016/j.jbi.2019.103307">https://doi.org/10.1016/j.jbi.2019.103307</a></p> <p><strong>RedMed embedding model:</strong></p> <p>Word vectors trained on comments from health related subreddits and optimized for drug synonym retrieval.</p> <p>The Redmed model was train using only social media data from Reddit and achieves comparable performance on the UMNSRS and MayoSRS similarity tasks. Vectors are 64 dimensional.</p> <p><strong><strong>redmed_model_vectors.tsv.gz - </strong></strong>Tab-separated word vectors (token\tdim1\tdim2\t...dim64)</p> <p><strong><strong>redmed_model.bin - </strong></strong>Binary word2vec file saved using gensim, can be loaded into python gensim</p> <p>Other Files:</p> <p>supp_file_1_sidebar_subreddits.txt - List of health-related subreddits based on "r/Health" and "r/Drugs" sidebars<br> supp_file_2_enrichment_based_subreddits.txt - List of health-related subreddits based on amount of health-related content<br> supp_file_3_custom_stopword_list.txt - List of stopwords based on counts derived from Reddit comments</p>
MeSDiCon - Medical Spanish Disease and symptom name Collection lexicon (unfiltered initial version)
<p>The MeSDiCon - (Medical Spanish Disease and symptom name Collection lexicon) consists of a list or gazetteer of candidate names of diseases and symptoms mentioned in Spanish clinical texts. Thus MeSDiCon serves as a lexical resource or dictionary for automatic detection of disease/symptom mentions, as well as indexing or classification of medical texts with such concept types.</p> <p>This collection was generated in a five step procedure:</p> <ol> <li>Automatic detection of mentions of disease/symptom terms in biomedical texts in English (including mapping/normalization to MeSH terms or OMIM identifiers).</li> <li>Generation of a unique name list from the detected concept mentions.</li> <li>Basic filtering of non- disease/symptom names or highly ambiguous mentions-abbreviations using basic characteristics like name morphology and length criteria.</li> <li>Automatic translation of name lists form English to Spanish using a medical machine translation system (see Soares, F. and Krallinger, M. BSC Participation in the WMT Translation of Biomedical Abstracts. In <em>Proceedings of the Fourth Conference on Machine Translation, Volume 3: Shared Task Papers, </em>pp. 175-178 2019; https://zenodo.org/record/3346802)</li> <li>Automatic mention lookup of translated names in a collection of 20 million Spanish clinical notes (primary care and pediatrics).</li> </ol> <p>Every term in MeSDiCon is identified by a text span (in Spanish), a target terminology namespace to which it was automatically mapped (MeSH or OMIM) and its corresponding concept identifier in that target terminology. Moreover, we provide for every text span the absolute term frequency, i.e. the number of matches in the corpus of 20 million clinical notes and the number of documents or notes in which it was automatically.</p> <p>Important note: no manual filtering of the MeSDiCon was carried out, implying that some entries might comprise errors, either due to the initial name recognition and concept mapping in English or due to wrong automatic translations into Spanish.</p> <p>The MeSDiCon resource is provided in two formats:</p> <ul> <li>TSV. Data is separated by tabs (\t). Every row of the file has the following fields:</li> </ul> <pre><code>terminology identifier translatedTerm termCount documentCount</code></pre> <ul> <li>JSON. Records are stored as a list of JSON objects. They have the following fields:</li> </ul> <pre><code>{ "terminology":"MESH", "identifier":"D025861", "translatedTerm":"Trastornos de la coagulación", "termFrequency":9, "documentFrequency":9 }</code></pre> <p> </p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
MeSCCon - Medical Spanish Chemical compound, drug and medication Name Lexicon (unfiltered version)
<p>The MeSCCon (Medical Spanish Chemical compound, drug and medication Name Lexicon) consists of a list or gazetteer of candidate names of chemicals, drugs, and medications mentioned in Spanish clinical texts. Thus MeSCCon serves as a lexical resource or dictionary for automatic detection of chemical/drug mentions, as well as indexing or classification of medical texts with such concept types.</p> <p>This collection was generated in a five step procedure:</p> <ol> <li>Automatic detection of mentions of chemicals and drugs in biomedical texts in English (including mapping/normalization to MeSH terms or ChEBI identifiers).</li> <li>Generation of a unique name list from the detected concept mentions.</li> <li>Basic filtering of non-chemical names or highly ambiguous mentions-abbreviations using basic characteristics like name morphology and length criteria.</li> <li>Automatic translation of name lists from English to Spanish using a medical machine translation system (see Soares, F. and Krallinger, M. BSC Participation in the WMT Translation of Biomedical Abstracts. In <em>Proceedings of the Fourth Conference on Machine Translation, Volume 3: Shared Task Papers, </em>pp. 175-178 2019; https://zenodo.org/record/3346802)</li> <li>Automatic mention lookup of translated names in a collection of 20 million Spanish clinical notes (primary care and pedriatrics).</li> </ol> <p>Every term in MeSCCon is identified by a text span (in Spanish), a target terminology namespace to which it was automatically mapped (MeSH or ChEBI) and the corresponding concept identifier in that terminology.</p> <p>Moreover, we provide for every text span the absolute term frequency, i.e. the number of matches in the corpus of 20 million clinical notes and the number of documents or notes in which it was found.</p> <p>Important note: no manual filtering of the MeSCCon was carried out, implying that some entries might comprise errors, either due to the initial name recognition and concept mapping in English or due to wrong automatic translations into Spanish.</p> <p>The MeSCCon resource is provided in two formats:</p> <ul> <li>TSV. Data is separated by tabs (\t). Every row of the file has the following fields:</li> </ul> <pre><code>terminology identifier translatedTerm termCount documentCount</code></pre> <ul> <li>JSON. Records are stored as a list of JSON objects. They have the following fields:</li> </ul> <pre><code class="language-javascript">{ "terminology":"MESH", "identifier":"D009020", "translatedTerm":"clorhidrato de morfina", "termFrequency":1, "documentFrequency":1 }</code></pre> <p> </p> <p>Copyright (c) 2019 Secretaría de Estado para el Avance Digital</p>
An Index to Roscher's Lexicon of Mythology
<p>compiled by Jonathan Groß <br><a href="https://orcid.org/0000-0002-2564-9530" target="_blank" rel="noopener">ORCID 0000-0002-2564-9530</a> <br><a href="mailto:jgross85@gmail.com" target="_blank" rel="noopener">jgross85 [AT] gmail [DOT] com</a></p> <h2>1. Introduction</h2> <h3>1.1. General Disclaimer</h3> <p>This index file was created as a private research project with the goal to make the wealth of information in Wilhelm Heinrich Roscher's "Detailed Lexicon of Greek and Roman Mythology" (Ausführliches Lexikon der griechischen und römischen Mythologie) more accessible to everybody.</p> <p>Roscher's Lexicon, originally published by B. G. Teubner in Leipzig from 1884 to 1937, is the most complete resource on Greek and Roman mythological names to date and also encompasses mythological (and religious) subjects from Sumeran, Akkadian, Babylonian, Hittite, Egyptian, Celtic, Germanic and other neighbouring cultures.</p> <p>The Lexicon was reprinted three times in the latter half of the 20th century (by Georg Olms in Hildesheim), even after its pictorial content had been superseded by the Lexicon Iconographicum Mythologiae Classicae (1981–1999, 2009). Unfortunately, since the last reprint of 1992/1993, Roscher's Lexicon has been out of stock at both publishers (Olms and De Gruyter Brill).</p> <p>Since the late 2000s, Roscher's Lexicon (the 6 main volumes and 4 supplements) was digitised by initiatives such as Google Books and the Internet Archive, and its contents can now be viewed there (with OCR text). One prominent use case of these scans is the German Wikipedia, where more than 2,500 pages use Roscher's Lexicon as a reference with a link to a scanned page in the Internet Archive.</p> <h3>1.2. Licensing</h3> <p>This dataset is released under the CC0 1.0 Universal License (https://creativecommons.org/publicdomain/zero/1.0/deed.en). I chose this license in order to maximise the usefulness of the data to everybody.</p> <p>Use and reuse of this data is strongly encouraged, and one use case has already been initiated by the author:<br>-https://mythogram.wikibase.cloud/wiki/Project:Roscher%27s_Lexicon_of_Mythology (presentation of the information from the index file as Linked Open Data, finished on 28 May 2024 with emendations until 5 August 2024)</p> <p>Although not technically required by the licensing agreement, the author would appreciate being informed about other uses of the data.</p> <p>The contents of the Lexicon themselves are mostly in the Public Domain as of 2024. Additionally, many of the smaller entries do not reach the threshold of originality. This includes most of the cover addenda.</p> <h2>2. Description of Data</h2> <p>The index file is formatted as tabular data. This file was created with LibreOffice Calc (originally in 7.6.6.3, in LibreOffice Calc 24.2 as of version 1.1 of this file) and is stored in its native .ods format. For convenience, an .xlsx version is also provided. Both files are practically identical, but the .ods file is to be regarded as the 'original'.</p> <p>Data is stored in several tabs:<br>(A) 'main alphabet' with the headwords of the main work (excluding addenda and corrigenda from the covers; for these see below).<br>(B) 'cover addenda' with the additional entries<br>(C) 'authors' with information on the authors<br>(D) 'fascicles' with information on the individual issues of the Lexicon</p> <p>The Tabs are available separately as .csv files (with tab separation, so strictly speaking it should be .tsv).</p> <p>Tabs A and B are almost identical in structure, with the columns:<br>A id = unique entry ID (not authoritative, just a means to identify individual entries)<br>B headword = lemma of the entry as stated by the Lexicon<br>C subject_type = classification scheme for the subject matter of the article (again, not authoritative and in places even contentious)<br>D vol = volume number<br>E fascicle = issue number (not found in most exemplars, assigned according to my own research)<br>F date = publication date of the entry (inferred from the issue date of the fascicle)<br>G–H col1,2 = start and end column<br>I colspan = span of columns<br>J–M author1,2,3,4 = author of the entry (please refer to Tab C, column A)<br>N entry_type = classification of entry (article, cross-reference, addendum, correction)<br>O scan = URL to a scan of the start column in the Internet Archive<br>P Wikidata = ID of the Wikidata item representing the subject (incomplete as of Version 1.1)<br>Q FactGrid = ID of the FactGrid item representing the subject (mostly missing as of Version 1.1)<br>R Mythogram = ID of the (bibliographic) Mythogram item representing the Lexicon entry<br>S redirect_target = target headword as stated, if the entry is a cross-reference<br>T remarks = remarks on the entry or subject (such as 'non-entity', 'duplicate', 'double lemma')<br>U PD = if the entry is in the Public Domain (either 'yes' or year where it enters the PD)</p> <p>Tab B has two additional columns, which are mostly empty as of version 1.1<br>V referring_to = target entry (in the main alphabet) of the correction or addenda<br>W excerpt = textual excerpt from the entry</p> <p>Tab C has information on the authors:<br>A short_name = for sorting reasons<br>B full_name = full name<br>C Wikidata = Wikidata item<br>D FactGrid = FactGrid item<br>E Mythogram = Mythogram item<br>F yob = year of birth<br>G yod = year of death<br>H vols = volumes contributed to<br>I article_count = number of articles written (not counting corrections and addenda from Tab B)<br>J–L namestring1,2,3 = name as written in the Lexicon<br>M remarks = remarks on completeness and certainty of data</p> <p>Tab D informs about the individual fascicles of the Lexicon as they appeared from 1884 to 1937:<br>A no. = fascicle number<br>B vol = volume(s) the fascicle belongs to<br>C colspan = column span of the fascicle<br>D headwords = headwords contained in the fascicle as advertised on the cover page<br>E issue_date = date of publication of the fascicle as stated on the cover page<br>F quires = quire numbers of the fascicle<br>G quire_count = quire count of the fascicle (calculated from column numbers: in some cases, at the end of a volume, quires were shortened, returning rational numbers here)<br>H remarks = remarks (in German)</p> <h2>3. Version History and Change Log</h2> <p>--------------------------------------------------<br>Version 1.1 (August 11th, 2024)<br>-Tab A, column P (Wikidata Q-ids): added 2,380 out of 15,489 = 15.4%)<br>-Tab A, column R (Mythogram Q-ids): completed<br>-Tab C: added data for author Wilhelm Windisch (translated Cumont's article on 'Mithras')<br>-minor corrections to some entries (typos)<br>-volume number changed from 3.2 to 3.1 for 125 entries (Pasikrateia–Peirithoos)<br>-fascicle number changed from 104/105 to 106/107 for the last 12 entries (Tameobrigus–Kerberos [Nachtrag])<br>-addition of 11 missed entries, values in column A renumbered accordingly</p> <p>--------------------------------------------------<br>Version 1.0 (May 4th, 2024)<br>-Tabs A–B with complete and checked data for columns A–N and S<br>-Tab A with complete data for column O<br>-Tabs C and D with complete data</p> <p>--------------------------------------------------<br>Prior to publication:</p> <p>-collection and checking of data (roughly 376 hours of work, started in July 2023 and finished on Star Wars Day 2024)</p>
CLDF dataset derived from Sūn's "Tibeto-Burman Phonology and Lexicon" from 1991
<p>Cite the source of the dataset as:</p> <blockquote> <p>Sūn, Hóngkāi 孙宏开 (1991): Zangmianyu yuyin he cihui 藏缅语音和词汇 [Tibeto-Burman phonology and lexicon]. Beijing: Chinese Social Sciences Press.</p> </blockquote>
A Lexicon of Emotions in Turkish
<p>A Turkish lexicon of emotions prepared for dictionary function of MaxQDA software. This lexicon has been prepared as a part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.896311” </p>
MOL - Multilingual Offensive Lexicon
<p>MOL - Multilingual Offensive Lexicon is a specialized lexicon for abusive language detection for low-resource languages. It consists of 1,000 explicit and implicit terms and expressions with pejorative connotations, manually identified by a specialist and annotated by three different experts with contextual information archiving high annotator human agreement (73% Kappa).</p> <p>Each term and expression from MOL contains a binary class: context-dependent offensiveness and context-independent offensiveness. For example, the term ``hypocrite'' is classified as context-independent offensiveness, since it is mostly found in the pejorative contexts of use. On the other hand, the term (``worm'') is classified as context-dependent offensiveness because it also may be found in both pejorative and non-pejorative contexts of use such as ``Politicians are like society worms'' and ``Reduces virus, worm, and unwanted access threats''.</p> <p>Finally, terms that showed strong potential to indicate hate speech targets were also annotated. For instance, ``slut'' and ``Jews from hell'' may indicate sexist and antisemitism comments. Finally, these terms and expressions, originally written in Portuguese, were manually translated by native speakers in English, Spanish, German, French, and Turkish. </p>
Italian Polarity Lexicon
<p><strong>Italian polarity lexicon</strong></p> <p>Annotator: Susanna Tron</p> <p><br> Prefixes from the appraisal theory:</p> <p>A appreciation <br> F emotion<br> J judgement</p> <p><br> Polarity Labels:</p> <p>A_POS, F_POS, J_POS positive<br> A_NEG, F_NEG, J_NEG negative</p> <p>INT intensifier<br> DIM diminisher<br> SHI shifter</p> <p><br> Statistics:</p> <p>1'626 nouns (370 POS, 1242 NEG, 7 INT, 2 DIM, 5 SHI) <br> 1'549 adjectives (421 POS, 1060 NEG, 55 INT,8 DIM,5 SHI)<br> 206 adverbs (76 POS, 78 NEG, 44 INT, 5 DIM, 3 SHI)<br> 156 verbs (29 POS, 126 NEG, 0 INT, 1 DIM, 0 SHI)</p> <p>alltogether 3538 entries</p> <p><br> Format:</p> <p>lemma,polarity,part of speech</p> <p><br> Part of Speech:</p> <p>noun<br> adjective<br> adverb<br> verb</p> <p>Examples:</p> <p>allegrissimo,F_POS,adjective<br> allergia,A_NEG,noun<br> altamente,INT,adjective<br> alterigia,J_NEG,noun</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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