Learning with Noisy Labels for Sentence-level Sentiment Classification
Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly. This paper studies the problem of learning with noisy labels for sentence-level sentiment classification. We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training. NetAb consists of two convolutional neural networks, one with a noise transition layer for dealing with the input noisy labels and the other for predicting 'clean' labels. We train the two networks using their respective loss functions in a mutual reinforcement manner. Experimental results demonstrate the effectiveness of the proposed model.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationLearning with noisy labelsSentenceSentiment AnalysisSentiment ClassificationSimilar Papers 제목 키워드 기반
A Statistical Parsing Framework for Sentiment Classification
We present a statistical parsing framework for sentence-level sentiment classification in this article. Unlike previous works that employ syntactic parsing results for sentiment analysis, we develop a statistical parser …
ClassificationGeneral ClassificationNegationSentence+2Annotated Corpus for Sentiment Analysis in Odia Language
Given the lack of an annotated corpus of non-traditional Odia literature which serves as the standard when it comes sentiment analysis, we have created an annotated corpus of Odia sentences and made it publicly available…
SentenceSentiment AnalysisLinguistic features for sentence difficulty prediction in ABSA
One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that …
Aspect-Based Sentiment AnalysisDiversityNatural Language UnderstandingPrediction+3Leveraging Multi-grained Sentiment Lexicon Information for Neural Sequence Models
Neural sequence models have achieved great success in sentence-level sentiment classification. However, some models are exceptionally complex or based on expensive features. Some other models recognize the value of exist…
General ClassificationNegationSentenceSentiment Analysis+1Improving Document-Level Sentiment Classification Using Importance of Sentences
Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that suppor…
ClassificationDocument ClassificationGeneral ClassificationSentence+4