Investigating LSTMs for Joint Extraction of Opinion Entities and Relations
We investigate the use of deep bidirectional LSTMs for joint extraction of opinion entities and the IS FROM and ISABOUT relations that connect them the first such attempt using a deep learning approach. Perhaps surprisingly, we find that standard LSTMs are not competitive with a state of the art CRF+ILP joint inference approach (Yang and Cardie, 2013) to opinion entities extraction, performing below even the standalone sequencetagging CRF. Incorporating sentence level and a novel relation level optimization, however, allows the LSTM to identify opinion relations and to perform within 1 3% of the state of the art joint model for opinion entities and the IS FROM relation; and to perform as well as the state of theart for the IS ABOUT relation all without access to opinion lexicons, parsers and other preprocessing components required for the feature rich CRF+ILP approach.
ACL