Abstract
This paper describes our system that competed at SemEval 2019 Task 9 - SubTask A: ”Sug- gestion Mining from Online Reviews and Forums”. Our system fuses the convolutional neural network and the latest BERT model to conduct suggestion mining. In our system, the input of convolutional neural network is the embedding vectors which are drawn from the pre-trained BERT model. And to enhance the effectiveness of the whole system, the pre-trained BERT model is fine-tuned by provided datasets before the procedure of embedding vectors extraction. Empirical results show the effectiveness of our model which obtained 9th position out of 34 teams with F1 score equals to 0.715.
Highlights
Suggestion mining is defined as the extraction of suggestions from unstructured text (Negi et al, 2018)
While suggestion mining is of great commercial value for organisations to improve the quality of their entities by considering the positive and negative opinions collected from platforms
The target of this task is to automatically classify the sentences collected from online reviews and forums into two classes which are suggestion and non-suggestion respectively (Negi et al, 2019)
Summary
Suggestion mining is defined as the extraction of suggestions from unstructured text (Negi et al, 2018). While suggestion mining is of great commercial value for organisations to improve the quality of their entities by considering the positive and negative opinions collected from platforms. The target of this task is to automatically classify the sentences collected from online reviews and forums into two classes which are suggestion and non-suggestion respectively (Negi et al, 2019). BERT which stands for Bidirectional Encoder Representation from Transformers is the latest breakthrough in the field of NLP provided by Google Research (Devlin et al, 2018). It provides a widely applicable tool for representation learning which can be generalized to many NLP tasks
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