1 research outputs found
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech 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