A novel scheme of domain transfer in document-level cross-domain sentiment classification

2021 ◽  
pp. 016555152110123
Author(s):  
Yueting Lei ◽  
Yanting Li
Keyword(s):  
Language Model ◽  
Sentiment Classification ◽  
Language Models ◽  
Product Reviews ◽  
Data Set ◽  
Emotional Words ◽  
Cross Domain ◽  
Text Ranking ◽  
Domain Transfer

The sentiment classification aims to learn sentiment features from the annotated corpus and automatically predict the sentiment polarity of new sentiment text. However, people have different ways of expressing feelings in different domains. Thus, there are important differences in the characteristics of sentimental distribution across different domains. At the same time, in certain specific domains, due to the high cost of corpus collection, there is no annotated corpus available for the classification of sentiment. Therefore, it is necessary to leverage or reuse existing annotated corpus for training. In this article, we proposed a new algorithm for extracting central sentiment sentences in product reviews, and improved the pre-trained language model Bidirectional Encoder Representations from Transformers (BERT) to achieve the domain transfer for cross-domain sentiment classification. We used various pre-training language models to prove the effectiveness of the newly proposed joint algorithm for text-ranking and emotional words extraction, and utilised Amazon product reviews data set to demonstrate the effectiveness of our proposed domain-transfer framework. The experimental results of 12 different cross-domain pairs showed that the new cross-domain classification method was significantly better than several popular cross-domain sentiment classification methods.

Tri-training for Dependency Parsing Domain Adaptation

2022 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Shu Jiang ◽  
Zuchao Li ◽  
Hai Zhao ◽  
Bao-Liang Lu ◽  
Rui Wang
Keyword(s):  
Transfer Learning ◽  
High Performance ◽  
Domain Adaptation ◽  
Language Model ◽  
Training Methods ◽  
Dependency Parsing ◽  
Cross Domain ◽  
Cross Lingual ◽  
Domain Transfer ◽  
Domain Transfer Learning

In recent years, the research on dependency parsing focuses on improving the accuracy of the domain-specific (in-domain) test datasets and has made remarkable progress. However, there are innumerable scenarios in the real world that are not covered by the dataset, namely, the out-of-domain dataset. As a result, parsers that perform well on the in-domain data usually suffer from significant performance degradation on the out-of-domain data. Therefore, to adapt the existing in-domain parsers with high performance to a new domain scenario, cross-domain transfer learning methods are essential to solve the domain problem in parsing. This paper examines two scenarios for cross-domain transfer learning: semi-supervised and unsupervised cross-domain transfer learning. Specifically, we adopt a pre-trained language model BERT for training on the source domain (in-domain) data at the subword level and introduce self-training methods varied from tri-training for these two scenarios. The evaluation results on the NLPCC-2019 shared task and universal dependency parsing task indicate the effectiveness of the adopted approaches on cross-domain transfer learning and show the potential of self-learning to cross-lingual transfer learning.

scholarly journals Learning Transferable Convolutional Proxy by SMI-Based Matching Technique

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Wei Jin ◽  
Nan Jia
Keyword(s):  
Density Ratio ◽  
Domain Model ◽  
Abnormal Behavior ◽  
Classification Error ◽  
Product Reviews ◽  
Target Domain ◽  
Data Set ◽  
Source Domain ◽  
Abnormal Behavior Detection ◽  
Domain Transfer

Domain-transfer learning is a machine learning task to explore a source domain data set to help the learning problem in a target domain. Usually, the source domain has sufficient labeled data, while the target domain does not. In this paper, we propose a novel domain-transfer convolutional model by mapping a target domain data sample to a proxy in the source domain and applying a source domain model to the proxy for the purpose of prediction. In our framework, we firstly represent both source and target domains to feature vectors by two convolutional neural networks and then construct a proxy for each target domain sample in the source domain space. The proxy is supposed to be matched to the corresponding target domain sample convolutional representation vector well. To measure the matching quality, we proposed to maximize their squared-loss mutual information (SMI) between the proxy and target domain samples. We further develop a novel neural SMI estimator based on a parametric density ratio estimation function. Moreover, we also propose to minimize the classification error of both source domain samples and target domain proxies. The classification responses are also smoothened by manifolds of both the source domain and proxy space. By minimizing an objective function of SMI, classification error, and manifold regularization, we learn the convolutional networks of both source and target domains. In this way, the proxy of a target domain sample can be matched to the source domain data and thus benefits from the rich supervision information of the source domain. We design an iterative algorithm to update the parameters alternately and test it over benchmark data sets of abnormal behavior detection in video, Amazon product reviews sentiment analysis, etc.

scholarly journals Image-based taxonomic classification of bulk biodiversity samples using deep learning and domain adaptation

2021 ◽  
Author(s):  
Tomochika Fujisawa ◽  
Victor Noguerales ◽  
Emmanouil Meramveliotakis ◽  
Anna Papadopoulou ◽  
Alfried P Vogler
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Domain Adaptation ◽  
Network Models ◽  
Neural Network Models ◽  
Data Set ◽  
Model Training ◽  
Trained Neural Network ◽  
Domain Transfer

Complex bulk samples of invertebrates from biodiversity surveys present a great challenge for taxonomic identification, especially if obtained from unexplored ecosystems. High-throughput imaging combined with machine learning for rapid classification could overcome this bottleneck. Developing such procedures requires that taxonomic labels from an existing source data set are used for model training and prediction of an unknown target sample. Yet the feasibility of transfer learning for the classification of unknown samples remains to be tested. Here, we assess the efficiency of deep learning and domain transfer algorithms for family-level classification of below-ground bulk samples of Coleoptera from understudied forests of Cyprus. We trained neural network models with images from local surveys versus global databases of above-ground samples from tropical forests and evaluated how prediction accuracy was affected by: (a) the quality and resolution of images, (b) the size and complexity of the training set and (c) the transferability of identifications across very disparate source-target pairs that do not share any species or genera. Within-dataset classification accuracy reached 98% and depended on the number and quality of training images and on dataset complexity. The accuracy of between-datasets predictions was reduced to a maximum of 82% and depended greatly on the standardisation of the imaging procedure. When the source and target images were of similar quality and resolution, albeit from different faunas, the reduction of accuracy was minimal. Application of algorithms for domain adaptation significantly improved the prediction performance of models trained by non-standardised, low-quality images. Our findings demonstrate that existing databases can be used to train models and successfully classify images from unexplored biota, when the imaging conditions and classification algorithms are carefully considered. Also, our results provide guidelines for data acquisition and algorithmic development for high-throughput image-based biodiversity surveys.

Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews

2011 ◽  
Vol 37 (3) ◽  
pp. 587-616 ◽  
Author(s):  
Xiaojun Wan
Keyword(s):  
Sentiment Classification ◽  
Research Progress ◽  
Product Reviews ◽  
Training Approach ◽  
Translation Services ◽  
Test Sets ◽  
Cross Lingual ◽  
The Web ◽  
Chinese View

The lack of reliable Chinese sentiment resources limits research progress on Chinese sentiment classification. However, there are many freely available English sentiment resources on the Web. This article focuses on the problem of cross-lingual sentiment classification, which leverages only available English resources for Chinese sentiment classification. We first investigate several basic methods (including lexicon-based methods and corpus-based methods) for cross-lingual sentiment classification by simply leveraging machine translation services to eliminate the language gap, and then propose a bilingual co-training approach to make use of both the English view and the Chinese view based on additional unlabeled Chinese data. Experimental results on two test sets show the effectiveness of the proposed approach, which can outperform basic methods and transductive methods.

scholarly journals Cross-Domain Sentiment Classification With Bidirectional Contextualized Transformer Language Models

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 163219-163230 ◽  
Author(s):  
Batsergelen Myagmar ◽  
Jie Li ◽  
Shigetomo Kimura
Keyword(s):  
Sentiment Classification ◽  
Language Models ◽  
Cross Domain
Sign in / Sign up

Export Citation Format

Share Document