Measuring cross-lingual semantic similarity across European languages
buir.contributor.author | Şenel, Lütfü Kerem | |
buir.contributor.author | Çukur, Tolga | |
dc.citation.epage | 363 | en_US |
dc.citation.spage | 359 | en_US |
dc.contributor.author | Şenel, Lütfü Kerem | en_US |
dc.contributor.author | Yücesoy, V. | en_US |
dc.contributor.author | Koç, A. | en_US |
dc.contributor.author | Çukur, Tolga | en_US |
dc.coverage.spatial | Barcelona, Spain | en_US |
dc.date.accessioned | 2018-04-12T11:46:58Z | |
dc.date.available | 2018-04-12T11:46:58Z | |
dc.date.issued | 2017 | en_US |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.department | National Magnetic Resonance Research Center (UMRAM) | en_US |
dc.department | Interdisciplinary Program in Neuroscience (NEUROSCIENCE) | en_US |
dc.department | Aysel Sabuncu Brain Research Center (BAM) | en_US |
dc.description | Date of Conference: 5-7 July 2017 | en_US |
dc.description | Conference Name: 40th International Conference on Telecommunications and Signal Processing, TSP 2017 | en_US |
dc.description.abstract | This paper studies cross-lingual semantic similarity (CLSS) between five European languages (i.e. English, French, German, Spanish and Italian) via unsupervised word embeddings from a cross-lingual lexicon. The vocabulary in each language is projected onto a separate high-dimensional vector space, and these vector spaces are then compared using several different distance measures (i.e., correlation, cosine etc.) to measure their pairwise semantic similarities between these languages. A substantial degree of similarity is observed between the vector spaces learned from corpora of the European languages. Null hypothesis testing and bootstrap methods (by resampling without replacement) are utilized to verify the results. | en_US |
dc.description.provenance | Made available in DSpace on 2018-04-12T11:46:58Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2017 | en |
dc.identifier.doi | 10.1109/TSP.2017.8076005 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/37656 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | https://doi.org/10.1109/TSP.2017.8076005 | en_US |
dc.source.title | Proceedings of the 40th International Conference on Telecommunications and Signal Processing, TSP 2017 | en_US |
dc.subject | Cross-lingual semantic similarity | en_US |
dc.subject | Language models | en_US |
dc.subject | Natural language processing | en_US |
dc.subject | Semantic similarity | en_US |
dc.subject | Word embedding | en_US |
dc.subject | Natural language processing systems | en_US |
dc.title | Measuring cross-lingual semantic similarity across European languages | en_US |
dc.type | Conference Paper | en_US |
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