3 research outputs found
Opinion mining using combinational approach for different domains
An increase in use of web produces large content of information about products. Online reviews are used to make decision by peoples. Opinion mining is vast research area in which different types of reviews are analyzed. Several issues are existing in this area. Domain adaptation is emerging issue in opinion mining. Labling of data for every domain is time consuming and costly task. Hence the need arises for model that train one domain and applied it on other domain reducing cost aswell as time. This is called domain adaptation which is addressed in this paper. Using maximum entropy and clustering technique source domains data is trained. Trained data from source domain is applied on target data to labeling purpose A result shows moderate accuracy for 5 fold cross validation and combination of source domains for Blitzer et al (2007) multi domain product dataset
MASKER: Masked Keyword Regularization for Reliable Text Classification
Pre-trained language models have achieved state-of-the-art accuracies on
various text classification tasks, e.g., sentiment analysis, natural language
inference, and semantic textual similarity. However, the reliability of the
fine-tuned text classifiers is an often underlooked performance criterion. For
instance, one may desire a model that can detect out-of-distribution (OOD)
samples (drawn far from training distribution) or be robust against domain
shifts. We claim that one central obstacle to the reliability is the
over-reliance of the model on a limited number of keywords, instead of looking
at the whole context. In particular, we find that (a) OOD samples often contain
in-distribution keywords, while (b) cross-domain samples may not always contain
keywords; over-relying on the keywords can be problematic for both cases. In
light of this observation, we propose a simple yet effective fine-tuning
method, coined masked keyword regularization (MASKER), that facilitates
context-based prediction. MASKER regularizes the model to reconstruct the
keywords from the rest of the words and make low-confidence predictions without
enough context. When applied to various pre-trained language models (e.g.,
BERT, RoBERTa, and ALBERT), we demonstrate that MASKER improves OOD detection
and cross-domain generalization without degrading classification accuracy. Code
is available at https://github.com/alinlab/MASKER.Comment: AAAI 2021. First two authors contributed equall