Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (C18-1)
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| Challenge: | Existing domain adaptation methods for sentiment analysis are sensitive to domain differences, resulting in classifiers that perform poorly on new domains. |
| Approach: | They propose a domain adaptation problem as an embedding projection task using two mono-domain embeddable spaces and a bi-domain space to project across domains and predict sentiment. |
| Outcome: | The proposed model performs better on domains similar to state-of-the-art methods while requiring longer training times. |
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| Challenge: | Cross-domain sentiment analysis methods reduce the domain gap by training generalizable classifiers for each domain . large interclass margins in source domain help to reduce the effect of "domain shift" in the target domain. |
| Approach: | They propose a domain adaptation method which induces large margins between data representations that belong to different classes in an embedding space. |
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Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification (D18-1)
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| Challenge: | Existing methods for cross-domain sentiment classification are difficult and costly . domain adaptation is difficult because data in source and target domains are drawn from different distributions. |
| Approach: | They propose a semi-supervised learning approach that minimizes the distance between source and target instances in embedded feature space. |
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Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)
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| Challenge: | Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words. |
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Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
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Domain Adapted Word Embeddings for Improved Sentiment Classification (P18-2)
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| Challenge: | Generic word embeddings are trained on large-scale generic corpora, while domain specific ones are trained only on data from a domain of interest. |
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Cross-Domain Sentiment Classification with Target Domain Specific Information (P18-1)
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| Challenge: | Existing methods for sentiment classification focus on learning domain-invariant representations . few of them pay attention to domain-specific information, which should also be informative. |
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Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable (P18-1)
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| Challenge: | Previously, domain adaptation approaches to bilingual tasks were proposed . we show that simple adaptation process involving only unlabeled text is highly effective . |
| Approach: | They propose a method for domain adaptation of bilingual word embeddings using unlabeled data . they then tailor a semi-supervised classification method from computer vision to these tasks . |
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Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis (2020.coling-main)
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| Challenge: | Cross-domain sentiment analysis is a hot topic in research and industry . domain-invariant representation learning (DIRL) is used to learn a feature representation across domains . but, when label distribution P(Y) shifts across domain, it degrades performance . |
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Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets (N18-4)
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| Challenge: | Existing word embeddings for sentiment analysis are limited in domain specific applications . generic word embeds are poor initialization for tasks on domain specific data sets. |
| Approach: | They propose to use word embeddings adapted for domain specific data sets in sentiment classification applications. |
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Learning Domain Representation for Multi-Domain Sentiment Classification (N18-1)
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| Challenge: | Training data for sentiment analysis is abundant in multiple domains, yet scarce for other domains. |
| Approach: | They propose to use domain-specific representations of input sentences to improve sentiment classification . they use a descriptor vector to map adversarially trained domain-general Bi-LSTM inputs into domain- specific representations . |
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