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Transferable Interactive Memory Network for Domain Adaptation in Fine-Grained Opinion Extraction
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
In fine-grained opinion mining, aspect and opinion terms extraction has become a fundamental task that provides key information for user-generated texts. Despite its importance, a lack of annotated resources in many domains impede the ability to train a precise model. Very few attempts have applied unsupervised domain adaptation methods to transfer fine-grained knowledge (in the word level) from some labeled source domain(s) to any unlabeled target domain. Existing methods depend on the
doi:10.1609/aaai.v33i01.33017192
fatcat:six4ap235jgdxkfw4p4leb4sbe