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2 results for “word sense annotation”
RUSSE'2018: Human-Annotated Sense-Disambiguated Word Contexts for Russian
<p>This dataset contains human-annotated sense identifiers for 2562 contexts of 20 words used in the <a href="https://russe.nlpub.org/2018/wsi/">RUSSE'2018</a> shared task on Word Sense Induction and Disambiguation for the Russian language; part of the <em>bts-rnc</em> evaluation dataset. These sense identifiers are disambiguated as according to the sense inventory of the <a href="http://gramota.ru/slovari/info/bts/">Large Explanatory Dictionary of Russian</a>.</p> <p>The annotation is done on December 1, 2017, on the <a href="https://tolokanyandex.com/">Yandex.Toloka</a> crowdsourcing platform. In particular, 80 pre-annotated contexts are used for training the human annotators, 2562 contexts are annotated by humans such that each context was annotated by 9 different annotators. The annotation reliability is indicated by a high value of Krippendorff's α = 0.83. After the annotation, every context was additionally inspected (“curated”) by the organizers of the shared task.</p> <p>The following words are represented: <em>акция</em> (action / stock), <em>байка</em> (yarn / tale), <em>гвоздика</em> (carnation / nail), <em>гипербола</em> (hyperbole), <em>град</em> (avalanche), <em>гусеница</em> (grub), <em>домино</em> (domino), <em>кабачок</em> (marrow / pub), <em>капот</em> (hood), <em>карьер</em> (mine / career), <em>кок</em> (cook), <em>крона</em> (top / crown), <em>круп</em> (croup), <em>мандарин</em> (mandarine), <em>рок</em> (fate / rock), <em>слог</em> (syllable), <em>стопка</em> (glass, stack), <em>таз</em> (bowl), <em>такса</em> (rate / badger-dog), <em>шах</em> (shah / check).</p> <p>The following files are included in this dataset:</p> <ul> <li>Toloka assignments (training: <em>tasks-train.tsv</em>, annotation: <em>tasks-test.tsv</em>)</li> <li>Toloka output (non-aggregated: <em>assignments_01-12-2017.tsv.xz</em>, aggregated: <em>aggregated_results_pool_1036853__2017_12_01.tsv</em>)</li> <li>annotator agreement report (<em>agreement.txt</em>)</li> <li>curated report (<em>report-curated.tsv.xz</em> and a supplementary file <em>tasks-eval.tsv.xz</em>)</li> <li>the final aggregated dataset (<em>bts-rnc-crowd.tsv</em>)</li> </ul> <p>The <em>bts-rnc-crowd.tsv</em> file has the following format: <em>id</em>, <em>lemma</em>, <em>sense_id</em>, <em>left</em> hand side context, <em>word</em> form, <em>right</em> hand side context, list of <em>senses</em>. The encoding is UTF-8 and the line breaks are LF (UNIX).</p>
DWUG DE Sense: A data set of historical word sense annotations in German
<p>This data collection contains a subset of <a href="https://zenodo.org/record/5543723">DWUG DE</a> word usage data annotated with classical word sense definitions (<em>DWUG DE Sense</em>, see <code>data/*/judgments_senses.csv</code>). From these annotations aggregated and cleaned sense labels were derived (<code>labels/*/labels_senses.csv</code>). From these labels we derived additional binary semantic proximity labels between use pairs ('0' for different sense, '1' for same sense, <code>labels/*/labels_proximity.csv</code>) and change labels reflecting sense changes between the two time periods from which word usages were sampled (<code>stats/*/stats_groupings.csv</code>).</p> <p>The sense labels were derived from the sense annotation by removing instances where not at least 2/3 annotators agree on the label (<code>maj_2</code>/<code>maj_3</code>). Note that the binary proximity labels were <em>derived</em> from the sense annotation, and not directly judged by humans (in contrast to other <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUG data sets</a>). Note that consequently also the change scores EARLIER, LATER and COMPARE were not calculated directly from human judgments, but from the inferred binary proximity labels. Please find the code aggregating and cleaning the data, deriving proximity labels and deriving change labels in the <a href="https://github.com/Garrafao/WUGs">WUG repository</a>.</p> <p>Please find more information on the provided data in the paper referenced below.</p> <p>Version: 1.0.1, 01.11.2024. Correct or remove some normalization and lemmatization errors in the uses. Updated references.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Frank D. Zamora-Reina, Felipe Bravo-Marquez, Nikolay Arefyev. 2024. <a href="https://doi.org/10.1007/s10579-024-09771-7">Sense Through Time: Diachronic Word Sense Annotations for Word Sense Induction and Lexical Semantic Change Detection</a>. Language Resources and Evaluation.</p> <p>Dominik Schlechtweg. 2023. <a href="http://dx.doi.org/10.18419/opus-12833">Human and Computational Measurement of Lexical Semantic Change</a>. PhD thesis. University of Stuttgart.</p>
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