Reducing Confusion in Active Learning for Part-Of-Speech Tagging
AbstractActive learning (AL) uses a data selection algorithm to select useful training samples to minimize annotation cost. This is now an essential tool for building low-resource syntactic analyzers such as part-of-speech (POS) taggers. Existing AL heuristics are generally designed...
Main Authors: | Aditi Chaudhary, Antonios Anastasopoulos, Zaid Sheikh, Graham Neubig |
---|---|
Format: | Article |
Language: | English |
Published: |
The MIT Press
2021-01-01
|
Series: | Transactions of the Association for Computational Linguistics |
Online Access: | https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00350/97781/Reducing-Confusion-in-Active-Learning-for-Part-Of |
Similar Items
-
Predicting confusions and intelligibility of noisy speech
by: Messing, David P. (David Patrick), 1979-
Published: (2008) -
Malay part-of-speech tagging: An me-based approach
by: Abu Bakar, Juhaida, et al.
Published: (2016) -
Multilingual Part-of-Speech Tagging Two Unsupervised Approaches
by: Naseem, Tahira, et al.
Published: (2011) -
How much can part-of-speech tagging help parsing?
by: Dalrymple, M
Published: (2006) -
How much can part-of-speech tagging help parsing?
by: Dalrymple, M
Published: (2006)