Previous research on sentiment analysis mainly focuses on binary or ternary sentiment analysis in monolingual texts. However, in today's social media such as micro-blogs, emotions are often expressed in bilingual or multilingual text called code-switching text, and people's emotions are complex, including happiness, sadness, angry, afraid, surprise, etc. Different emotions may exist together, and the proportion of each emotion in the code-switching text is often unbalanced. Inspired by the recently proposed BERT model, we investigate how to fine-tune BERT for multi-label sentiment analysis in codeswitching text in this paper. Our investigation includes the selection of pre-trained models and the finetuning methods of BERT on this task. To deal with the problem of the unbalanced distribution of emotions, a method based on data augmentation, undersampling and ensemble learning is proposed to get balanced samples and train different multi-label BERT classifiers. Our model combines the prediction of each classifier to get the final outputs. The experiment on the dataset of NLPCC 2018 shared task 1 shows the effectiveness of our model for the unbalanced code-switching text. The F1-Score of our model is higher than many previous models.
SUMMARY
Similarity Knowledge Flow (SKF) is a kind of scientific workflow, providing an effective technique and theoretical support for intelligent browsing in the
Supervised neural network models have achieved outstanding performance in the document summarization task in recent years. However, it is hard to get enough labeled training data with a high quality for these models to generate different types of summaries in reality. In this work, we mainly focus on improving the performance of the popular unsupervised Textrank algorithm that requires no labeled training data for extractive summarization. We first modify the original edge weight of Textrank to take the relative position of sentences into account, and then combine the output of the improved Textrank with K-means clustering to improve the diversity of generated summaries. To further improve the performance of our model, we innovatively incorporate external knowledge from open-source knowledge graphs into our model by entity linking. We use the knowledge graph sentence embedding and the tf-idf embedding as the input of our improved Textrank, and get the final score for each sentence by linear combination. Evaluations on the New York Times data set show the effectiveness of our knowledge-enhanced approach. The proposed model outperforms other popular unsupervised models significantly.
Web service compositions run in changing environment where different context events can arise to affect the execution of services. In order not to make service execution affected by context events, context-aware service composition becomes one of the major research trends. Service providers can develop context-aware services which can adapt their behaviors dynamically to execution contexts. However, it burdens service providers because they have to keep in mind different execution contexts where their services could be used. In this paper, we design and implement a self-adaptive and context-aware service composition system which can adapt to changing execution contexts and make adjustments according to context events and user-defined personalized policies. It frees service providers from context handling which in turn becomes a task of our system. We use OWL to model context ontologies and extend the OWL-S service model to support context information. Policy is a userdefined adjustment strategy to guide the dynamic adaptation. Service consumers can submit their requests and get contextaware services. Our system can composite services according to service consumers' requests, execute services, monitor execution contexts and adjust its action when contexts change.
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