An Empirical Study On Identifying Sentences With Salient Factual Statements

2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)(2018)

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摘要
In this paper, we show that by using a relatively simple neural network architecture and including edge (i.e., nonsensical) cases into a dataset we can more reliably identify factual claims than predecessor SVM models. Doping the dataset with these nonsensical example results in a more robust model overall that is resistant to being tricked into classifying sentences into a certain category based on easily met criteria. Furthermore, we show that the use of multiple word-embeddings makes little difference to the overall accuracy of the model, but particular embeddings perform differently on text that contains digits (i.e., 0-9) which can be leveraged by using multiple models to come to a conclusion on the score for a particular piece of text. Our results also show, that for our particular dataset trying to differentiate sentences into more than two categories might hurt the overall accuracy of the models, or at least not provide any substantial benefits compared to the binary classification scenario.
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关键词
salient factual statements,multiple word-embeddings,neural network architecture,sentences identification,sentences classification
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