We investigate the use of deep bidirectional LSTMs for joint extraction of opinion entities and the IS-FROM and IS-ABOUT 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.
In our previous work REF , as explained earlier, we proposed a LSTM-based model for joint extraction of opinion entities and relations, but no relation types.
Investigating LSTMs for Joint Extraction of Opinion Entities and Relations
ACL
Computer Science