Efficient supervised and semi-supervised approaches for affiliations disambiguation
نویسندگان
چکیده
منابع مشابه
Word Sense Disambiguation with Semi-Supervised Learning
Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semisupervised learning algorithms are evaluated on 2...
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متن کاملSemi-Supervised Learning for Word Sense Disambiguation: Quality vs. Quantity
In this paper, we discuss the importance of the quality against the quantity of automatically extracted examples for word sense disambiguation (WSD). We first show that we can build a competitive WSD system with a memory-based classifier and a feature set reduced to easily and efficiently computable features. We then show that adding automatically annotated examples improves the performance of ...
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Word sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying the appropriate sense of a word in a sentence, when the word has multiple meanings. Many approaches have been proposed to solve the problem, of which supervised learning approaches are the most successful. However supervised machine learning are limited by the difficulties...
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ژورنال
عنوان ژورنال: Scientometrics
سال: 2013
ISSN: 0138-9130,1588-2861
DOI: 10.1007/s11192-013-1025-5