Active Learning to Remove Source Instances for Domain Adaptation for Word Sense Disambiguation

2016 ◽  
pp. 97-107 ◽  
Author(s):  
Hiroyuki Shinnou ◽  
Yoshiyuki Onodera ◽  
Minoru Sasaki ◽  
Kanako Komiya
Keyword(s):  
Active Learning ◽  
Domain Adaptation ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation

scholarly journals Domain Adaptation using Word Embeddings for Word Sense Disambiguation

2018 ◽  
Vol 25 (4) ◽  
pp. 463-480
Author(s):  
Kanako Komiya ◽  
Minoru Sasaki ◽  
Hiroyuki Shinnou ◽  
Manabu Okumura
Keyword(s):  
Domain Adaptation ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Word Embeddings ◽  
Sense Disambiguation

scholarly journals Interactive medical word sense disambiguation through informed learning

2018 ◽  
Vol 25 (7) ◽  
pp. 800-808 ◽  
Author(s):  
Yue Wang ◽  
Kai Zheng ◽  
Hua Xu ◽  
Qiaozhu Mei
Keyword(s):  
Active Learning ◽  
Learning Curve ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Interactive Learning ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
High Quality ◽  
Clinical Notes ◽  
Sense Disambiguation

Abstract Objective Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert’s domain knowledge in building high-quality medical WSD models with minimal human effort. Methods We developed an interactive learning algorithm with expert labeling instances and features. An expert can provide supervision in 3 ways: labeling instances, specifying indicative words of a sense, and highlighting supporting evidence in a labeled instance. The algorithm learns from these labels and iteratively selects the most informative instances to ask for future labels. Our evaluation used 3 WSD corpora: 198 ambiguous terms from Medical Subject Headings (MSH) as MEDLINE indexing terms, 74 ambiguous abbreviations in clinical notes from the University of Minnesota (UMN), and 24 ambiguous abbreviations in clinical notes from Vanderbilt University Hospital (VUH). For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy on the test set against the number of labeled instances was generated. The area under the learning curve was used as the primary evaluation metric. Results Our interactive learning algorithm significantly outperformed active learning, the previous fastest learning algorithm for medical WSD. Compared to active learning, it achieved 90% accuracy for the MSH corpus with 42% less labeling effort, 35% less labeling effort for the UMN corpus, and 16% less labeling effort for the VUH corpus. Conclusions High-quality WSD models can be efficiently trained with minimal supervision by inviting experts to label informative instances and provide domain knowledge through labeling/highlighting contextual features.

scholarly journals Applying active learning to supervised word sense disambiguation in MEDLINE

2013 ◽  
Vol 20 (5) ◽  
pp. 1001-1006 ◽  
Author(s):  
Yukun Chen ◽  
Hongxin Cao ◽  
Qiaozhu Mei ◽  
Kai Zheng ◽  
Hua Xu
Keyword(s):  
Active Learning ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation

scholarly journals A combination of active learning and semi-supervised learning starting with positive and unlabeled examples for word sense disambiguation

2009 ◽  
Author(s):  
Makoto Imamura ◽  
Yasuhiro Takayama ◽  
Nobuhiro Kaji ◽  
Masashi Toyoda ◽  
Masaru Kitsuregawa
Keyword(s):  
Active Learning ◽  
Supervised Learning ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation
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