Our work, which employs CNN-and LSTM-based deep neural networks, is inspired by advances in sentence classification (Kim, 2014) and sequence classification (Hochreiter and Schmidhuber, 1997) using distributional word representations and deep learning.
our approach leverages word2vec 1 distributional embeddings, dependency context-based embeddings (Levy and Goldberg, 2014), and factuality/certainty-indicating embeddings for improving claim classification.