INK-USC/PLE
Label Noise Reduction in Entity Typing (KDD'16)
This project helps identify and categorize entities (like people, organizations, or locations) within large volumes of text, even when the initial labels are inconsistent or inaccurate. It takes a text corpus with detected entities and rough labels as input and outputs the same text with refined, more accurate entity types. Researchers and data analysts working with text data who need highly precise entity recognition for downstream tasks would find this valuable.
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Use this if you need to clean up noisy, automatically generated labels for entities in your text data to improve the accuracy of fine-grained entity typing.
Not ideal if you need an end-to-end tool that takes raw text and outputs typed entities directly, as entity detection and initial labeling are assumed to be pre-done.
Stars
53
Forks
14
Language
C++
License
GPL-3.0
Category
Last pushed
Oct 23, 2017
Commits (30d)
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