2015/09/04 by Parminder Bhatia, Bhatia, Parminder, Yangfeng Ji +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.1509.01599
Published at Empirical Methods in Natural Language Processing (EMNLP 2015)
arxiv created 2015/09/11 · arxiv updated 2015/09/14
Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level sentiment analysis, via composition of local information up the discourse tree. First, we show that reweighting discourse units according to their position in a dependency representation of the rhetorical structure can yield substantial improvements on lexicon-based sentiment analysis. Next, we present a recursive neural network over the RST structure, which offers significant improvements over classification-based methods.