Nothing Special   »   [go: up one dir, main page]

×
Please click here if you are not redirected within a few seconds.
The lack of positive results on super- vised domain adaptation for WSD have cast some doubts on the utility of hand- tagging general corpora and thus devel-.
The lack of positive results on super- vised domain adaptation for WSD have cast some doubts on the utility of hand- tagging general corpora and thus devel-.
This paper shows for the first time that a WSD system trained on a general source corpus and the target corpus, obtains up to 22% error reduction when ...
The lack of positive results on supervised domain adaptation for WSD have cast some doubts on the utility of hand-tagging general corpora and thus developing ...
This paper proposes a word-by-word model selection approach to domain adaptation for Word Sense Disambiguation. By this approach, the model for a target ...
In this paper we show for the first time that our WSD system trained on a general source corpus (Bnc) and the target corpus, obtains up to 22% error reduction ...
Domain adaptation is important for WSD as well as many other Natural Language Processing (NLP) tasks [1], [2]. State- of-the-art supervised WSD systems perform ...
People also ask
Experimental results on a domain-specific corpus show that the improved self-training model is effective for the words which have target domain linked ...
Domain adaptation. Domain Adaptation is a field of associated with machine learning and transfer learning. This scenario arises when we aim at learning from ...
We call this framework Active Learning Domain Adapted (Alda). Our pro- posed framework is based on three key components. The first component is unsupervised ...
Missing: WSD. | Show results with:WSD.