Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Oshin Agarwal"'
Autor:
Oshin Agarwal, Ani Nenkova
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 10, Pp 904-921 (2022)
AbstractKeeping the performance of language technologies optimal as time passes is of great practical interest. We study temporal effects on model performance on downstream language tasks, establishing a nuanced terminology for such discussion and id
Externí odkaz:
https://doaj.org/article/1768e8fadbde48c7a64c4d5ff698da50
Publikováno v:
Computational Linguistics, Vol 47, Iss 1, Pp 117-140 (2021)
AbstractNamed entity recognition systems achieve remarkable performance on domains such as English news. It is natural to ask: What are these models actually learning to achieve this? Are they merely memorizing the names themselves? Or are they capab
Externí odkaz:
https://doaj.org/article/78ad4f62982946e3bf91be0618ac8b5a
Autor:
Oshin Agarwal
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 36:12866-12867
Named Entity Recognition models perform well on benchmark datasets but fail to generalize well even in the same domain. The goal of my th esis is to quantify the degree of in-domain generalization in NER, probe models for entity name vs. context lear
Publikováno v:
EACL
The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists. Computational tools for detecting online incivility for English are now fairly accessible and potentially could be applied
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7da7161d8dfe737983a87998f5fd3e46
Autor:
Ani Nenkova, Oshin Agarwal
Publikováno v:
ACL/IJCNLP (Findings)
Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training
Publikováno v:
NAACL-HLT
Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets. In this paper, however, we verbalize the entire English Wikidata KG, and discuss the unique c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b5bb6865d8bb8ce3837661e8cf7df79c
http://arxiv.org/abs/2010.12688
http://arxiv.org/abs/2010.12688
Publikováno v:
NAACL-HLT (1)
Modern NLP systems require high-quality annotated data. In specialized domains, expert annotations may be prohibitively expensive. An alternative is to rely on crowdsourcing to reduce costs at the risk of introducing noise. In this paper we demonstra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c8dc27eae577d2c5194a8f539a225c2
Publikováno v:
Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference.
In many NLP applications like search and information extraction for named entities, it is necessary to find all the mentions of a named entity, some of which appear as pronouns (she, his, etc.) or nominals (the professor, the German chancellor, etc.)
Publikováno v:
SEM@NAACL-HLT
Word representations trained on text reproduce human implicit bias related to gender, race and age. Methods have been developed to remove such bias. Here, we present results that show that human stereotypes exist even for much more nuanced judgments
Autor:
Ani Nenkova, Soham Parikh, Byron C. Wallace, Iain J. Marshall, Oshin Agarwal, Elizabeth Conrad
Publikováno v:
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications.
Standard paradigms for search do not work well in the medical context. Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect t