Abstract
This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.- Anthology ID:
- 2022.coling-1.472
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 5309–5326
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.472
- DOI:
- Bibkey:
- Cite (ACL):
- Georgie Botev, Arya D. McCarthy, Winston Wu, and David Yarowsky. 2022. Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5309–5326, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages (Botev et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.472.pdf
Export citation
@inproceedings{botev-etal-2022-deciphering,
title = "Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages",
author = "Botev, Georgie and
McCarthy, Arya D. and
Wu, Winston and
Yarowsky, David",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.472",
pages = "5309--5326",
abstract = "This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples.",
}
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%0 Conference Proceedings %T Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages %A Botev, Georgie %A McCarthy, Arya D. %A Wu, Winston %A Yarowsky, David %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F botev-etal-2022-deciphering %X This paper presents a detailed foundational empirical case study of the nature of out-of-vocabulary words encountered in modern text in a moderate-resource language such as Bulgarian, and a multi-faceted distributional analysis of the underlying word-formation processes that can aid in their compositional translation, tagging, parsing, language modeling, and other NLP tasks. Given that out-of-vocabulary (OOV) words generally present a key open challenge to NLP and machine translation systems, especially toward the lower limit of resource availability, there are useful practical insights, as well as corpus-linguistic insights, from both a detailed manual and automatic taxonomic analysis of the types, multidimensional properties, and processing potential for multiple representative OOV data samples. %U https://aclanthology.org/2022.coling-1.472 %P 5309-5326
Markdown (Informal)
[Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages](https://aclanthology.org/2022.coling-1.472) (Botev et al., COLING 2022)
- Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages (Botev et al., COLING 2022)
ACL
- Georgie Botev, Arya D. McCarthy, Winston Wu, and David Yarowsky. 2022. Deciphering and Characterizing Out-of-Vocabulary Words for Morphologically Rich Languages. In Proceedings of the 29th International Conference on Computational Linguistics, pages 5309–5326, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.