Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/459884
Title: | Understanding and Mitigation of Noise in Crowd Sourced Relation Classification Dataset |
Researcher: | Parekh, Akshay |
Guide(s): | Awekar, Amit and Anand, Ashish |
Keywords: | Computer Science Computer Science Artificial Intelligence Engineering and Technology |
University: | Indian Institute of Technology Guwahati |
Completed Date: | 2023 |
Abstract: | Relation classification (RC), a task of classifying the relation between a given pair of entities in a sentence to a relation newlinelabel is fundamental to IE systems. The identified structured triple (subject_entity, relation, object_entity) from the newlineunstructured text can vastly help in knowledge base completion. This organized relational knowledge can further be newlineused for other downstream tasks like question-answering, and common-sense reasoning. A large RC dataset TACRED newlinehas been widely used for benchmarking modern deep neural models. However, RC at a large scale is restricted mainly due to the presence of noise in the training dataset. Hence, the performance of such advanced deep neural models, which have shown excellent improvement on other NLP tasks, has been held back for RC. |
Pagination: | |
URI: | http://hdl.handle.net/10603/459884 |
Appears in Departments: | Department of Computer Science and Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_fulltext.pdf | Attached File | 2.53 MB | Adobe PDF | View/Open |
04_abstract.pdf | 192.94 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 217.06 kB | Adobe PDF | View/Open |
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