| 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. |
| Approach: | They propose a method to combine breadth of generic and specific embeddings to form domain-specific embeddables. |
| Outcome: | The proposed method outperforms generic and domain specific embeddings on sentiment classification tasks. |
Similar Papers
Shallow Domain Adaptive Embeddings for Sentiment Analysis (D19-1)
Copied to clipboard
| Challenge: | Existing domain adaptation algorithms for text classification are limited by lack of training data and exploiting domain idiosyncrasies to improve performance. |
| Approach: | They propose a domain adaptation layer that learns weights to combine a generic and a specific word embedding into a DA embeddable. |
| Outcome: | The proposed approach improves on binary and multi-class classification tasks using popular encoder architectures. |
Learning Word Embeddings for Data Sparse and Sentiment Rich Data Sets (N18-4)
Copied to clipboard
| 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. |
| Outcome: | The proposed algorithms learn word embeddings on sparse and sentiment rich data sets. |
Domain Adaptation for Sentiment Analysis Using Robust Internal Representations (2023.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | The proposed method reduces the domain gap by training cross-domain generalizable classifiers . large interclass margins in the source domain help reduce the effect of "domain shift" the proposed method is available in the u.s. |
Projecting Embeddings for Domain Adaption: Joint Modeling of Sentiment Analysis in Diverse Domains (C18-1)
Copied to clipboard
| 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. |
Simple Algorithms For Sentiment Analysis On Sentiment Rich, Data Poor Domains. (C18-1)
Copied to clipboard
| Challenge: | Standard word embedding algorithms learn vector representations from large corpora of text documents in unsupervised fashion. |
| Approach: | They propose an algorithm that learns word embeddings jointly with a classifier . their algorithm leverages document label information to learn vector representations of words . |
| Outcome: | The proposed algorithm has superior performance on domains with limited data compared to other methods. |
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings (P18-1)
Copied to clipboard
| Challenge: | Existing word embeddings cannot produce domain-sensitive embeddables due to domain-specific nature of words. |
| Approach: | They propose a method for learning domain-sensitive and sentiment-aware embeddings that captures sentiment semantics and domain sensitivity of individual words. |
| Outcome: | The proposed method can produce domain-common embeddings and domain-specific embedds. |
Two Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable (P18-1)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method improves on two bilingual tasks using unlabeled data. |
Task-oriented Domain-specific Meta-Embedding for Text Classification (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods neglect domain-specific knowledge and use the same word embedding for each word in all domain-specified datasets. |
| Approach: | They propose a method to incorporate domain-specific and task-oriented information into meta-embeddings by combining pre-trained word embeddings. |
| Outcome: | The proposed method performs well on four text classification datasets and shows that it is compatible with existing methods. |
Sentiment Classification Using Document Embeddings Trained with Cosine Similarity (P19-2)
Copied to clipboard
| Challenge: | Existing document embedding models map each document to a dense, low-dimensional vector in continuous vector space. |
| Approach: | They propose to train document embeddings using cosine similarity instead of dot product . they focus on document sentiment classification of long movie reviews . |
| Outcome: | The proposed model improves document embedding accuracy by using cosine similarity instead of dot product on the IMDB dataset while using feature combination with Naive Bayes weighted bag of n-grams achieves 93.68% accuracy. |
PERL: Pivot-based Domain Adaptation for Pre-trained Deep Contextualized Embedding Models (2020.tacl-1)
Copied to clipboard
| Challenge: | PERL is a representation learning model that uses labeled data from the source domain and unlabeled data not necessarily drawn from the target domain. |
| Approach: | They propose a model that extends contextualized word embedding models with pivot-based fine-tuning to address this bottleneck. |
| Outcome: | The proposed model outperforms strong baselines across 22 sentiment classification domain adaptation setups and improves in-domain model performance. |