Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings
This work addresses a less investigated sentence classification task for natural language processing, but it is incremental as it builds on existing LSTM architectures and distant supervision techniques.
The paper tackled the problem of suggestion mining, which lacks hand-labeled benchmark datasets, by proposing two distant supervision approaches using a silver standard dataset from wikiHow and Wikipedia, with the best method achieving improved classification accuracy through POS embeddings.
Mining suggestion expressing sentences from a given text is a less investigated sentence classification task, and therefore lacks hand labeled benchmark datasets. In this work, we propose and evaluate two approaches for distant supervision in suggestion mining. The distant supervision is obtained through a large silver standard dataset, constructed using the text from wikiHow and Wikipedia. Both the approaches use a LSTM based neural network architecture to learn a classification model for suggestion mining, but vary in their method to use the silver standard dataset. The first approach directly trains the classifier using this dataset, while the second approach only learns word embeddings from this dataset. In the second approach, we also learn POS embeddings, which interestingly gives the best classification accuracy.