Czech Dataset for Semantic Similarity and Relatedness
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017 (2017)
Research topics:
Semantic AnalysisAbstract
This paper introduces a Czech dataset for semantic similarity and semantic relatedness. The dataset contains word pairs with hand annotated scores that indicate the semantic similarity and semantic relatedness of the words. The dataset contains 953 word pairs compiled from 9 different sources. It contains words and their contexts taken from real text corpora including extra examples when the words are ambiguous. The dataset is annotated by 5 independent annotators. The average Spearman correlation coefficient of the annotation agreement is r = 0.81. We provide reference evaluation experiments with several methods for computing semantic similarity and relatedness.
Authors
BibTex
@InProceedings{ranlp2017a, author = {Konopik, Miloslav and Pra\v{z}\'{a}k, Ond\v{r}ej and Steinberger, David}, title = {Czech Dataset for Semantic Similarity and Relatedness}, booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017}, month = {September}, year = {2017}, address = {Varna, Bulgaria}, publisher = {INCOMA Ltd.}, pages = {401--406}, abstract = {This paper introduces a Czech dataset for semantic similarity and semantic relatedness. The dataset contains word pairs with hand annotated scores that indicate the semantic similarity and semantic relatedness of the words. The dataset contains 953 word pairs compiled from 9 different sources. It contains words and their contexts taken from real text corpora including extra examples when the words are ambiguous. The dataset is annotated by 5 independent annotators. The average Spearman correlation coefficient of the annotation agreement is r = 0.81. We provide reference evaluation experiments with several methods for computing semantic similarity and relatedness.}, url = {https://doi.org/10.26615/978-954-452-049-6_053} }
