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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 30 1 Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification Nedumpozhimana, Vasudevan; Klubička, Filip; Kelleher, John D. In: Front Artif Intell (2022) BASE Show details 2 Semantic Relatedness and Taxonomic Word Embeddings ... Kacmajor, Magdalena; Kelleher, John D.; Klubicka, Filip. - : arXiv, 2020 BASE Show details 3 English WordNet Taxonomic Random Walk Pseudo-Corpora Klubicka, Filip; Maldonado, Alfredo; Mahalunkar, Abhijit... In: Conference papers (2020) BASE Show details 4 Language related issues for machine translation between closely related south Slavic languages Arcan, Mihael; Klubicka, Filip; Popovic, Maja. - : The COLING 2016 Organizing Committee, 2019 BASE Show details 5 Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance Klubicka, Filip; Mahalunkar, Abhijit; Maldonado, Alfredo... In: Conference papers (2019) BASE Show details 6 Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings Maldonado, Alfredo; Klubicka, Filip; Kelleher, John D. In: Articles (2019) Abstract: Word embeddings trained on natural corpora (e.g., newspaper collections, Wikipedia or the Web) excel in capturing thematic similarity (“topical relatedness”) on word pairs such as ‘coffee’ and ‘cup’ or ’bus’ and ‘road’. However, they are less successful on pairs showing taxonomic similarity, like ‘cup’ and ‘mug’ (near synonyms) or ‘bus’ and ‘train’ (types of public transport). Moreover, purely taxonomy-based embeddings (e.g. those trained on a random-walk of WordNet’s structure) outperform natural-corpus embeddings in taxonomic similarity but underperform them in thematic similarity. Previous work suggests that performance gains in both types of similarity can be achieved by enriching natural-corpus embeddings with taxonomic information from taxonomies like WordNet. This taxonomic enrichment can be done by combining natural-corpus embeddings with taxonomic embeddings (e.g. those trained on a random-walk of WordNet’s structure). This paper conducts a deep analysis of this assumption and shows that both the size of the natural corpus and of the random-walk coverage of the WordNet structure play a crucial role in the performance of combined (enriched) vectors in both similarity tasks. Specifically, we show that embeddings trained on medium-sized natural corpora benefit the most from taxonomic enrichment whilst embeddings trained on large natural corpora only benefit from this enrichment when evaluated on taxonomic similarity tasks. The implication of this is that care has to be taken in controlling the size of the natural corpus and the size of the random-walk used to train vectors. In addition, we find that, whilst the WordNet structure is finite and it is possible to fully traverse it in a single pass, the repetition of well-connected WordNet concepts in extended random-walks effectively reinforces taxonomic relations in the learned embeddings. Keyword: Computational Engineering; Computational Linguistics; retrofitting; semantic similarity; taxonomic embeddings; taxonomic enrichment; word embeddings; WordNet URL: https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1090&context=scschcomart https://arrow.tudublin.ie/scschcomart/83 BASE Hide details 7 Training corpus hr500k 1.0 Ljubešić, Nikola; Agić, Željko; Klubička, Filip. - : Jožef Stefan Institute, 2018 BASE Show details 8 Quantitative Fine-Grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian ... Klubička, Filip; Toral, Antonio; Sánchez-Cartagena, Víctor M.. - : arXiv, 2018 BASE Show details 9 Is it worth it? Budget-related evaluation metrics for model selection ... Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018 BASE Show details 10 Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian Sanchez-Cartagena, Victor Manuel; Toral, Antonio; Klubicka, Filip In: Articles (2018) BASE Show details 11 Is it worth it? Budget-related evaluation metrics for model selection Klubicka, Filip; Salton, Giancarlo; Kelleher, John D. In: Conference papers (2018) BASE Show details 12 hr500k – A Reference Training Corpus of Croatian. Erjavec, Tomaž; Ljubešić, Nikola; Klubicka, Filip... In: Conference papers (2018) BASE Show details 13 Croatian Twitter training corpus ReLDI-NormTag-hr 1.1 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 14 Serbian Twitter training corpus ReLDI-NormTag-sr 1.0 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 15 Croatian Twitter training corpus ReLDI-NormTag-hr 1.0 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 16 Serbian Twitter training corpus ReLDI-NormTag-sr 1.1 Ljubešić, Nikola; Farkaš, Daša; Klubička, Filip. - : Jožef Stefan Institute, 2017 BASE Show details 17 Fine-grained human evaluation of neural versus phrase-based machine translation ... Klubička, Filip; Toral, Antonio; Sánchez-Cartagena, Víctor M.. - : arXiv, 2017 BASE Show details 18 Fine-Grained Human Evaluation of Neural Versus Phrase-Based Machine Translation Klubička Filip; Toral Antonio; Sánchez-Cartagena Víctor M. In: Prague Bulletin of Mathematical Linguistics , Vol 108, Iss 1, Pp 121-132 (2017) (2017) BASE Show details 19 Serbian-English parallel corpus srenWaC 1.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details 20 Finnish-English parallel corpus fienWaC 1.0 Ljubešić, Nikola; Esplà-Gomis, Miquel; Ortiz Rojas, Sergio. - : Jožef Stefan Institute, 2016 BASE Show details Page: 1 2

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