Investigating LSTMs for Joint Extraction of Opinion Entities and Relations

Abstract

We investigate the use of deep bi-directional 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 sequence-tagging 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-the-art for the IS - ABOUT relation — all without access to opinion lexicons, parsers and other preprocessing components required for the feature-rich CRF+ILP approach.