Investigating LSTMs for Joint Extraction of Opinion Entities and Relations

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 919–929, Berlin, Germany, August 7-12, 2016. c⃝2016 Association for Computational Linguistics Investigating LSTMs for Joint Extraction of Opinion Entities and Relations Arzoo Katiyar and Claire Cardie Department of Computer Science Cornell University Ithaca, NY, 14853, USA arzoo, cardie@cs.cornell.edu 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 in- ference approach (Yang and Cardie, 2013) to opinion entities extraction, perform- ing 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 with- out access to opinion lexicons, parsers and other preprocessing components required for the feature-rich CRF+ILP approach. 1 Introduction There has been much research in recent years in the area of fine-grained opinion analysis where the goal is to identify subjective expressions in text along with their associated sources and tar- gets. More specifically, fine-grained opinion anal- ysis aims to identify three types of opinion enti- ties: • opinion expressions, O, which are direct subjective expressions (i.e., explicit mentions of otherwise private states or speech events expressing private states (Wiebe and Cardie, 2005)); • opinion targets, T, which are the entities or topics that the opinion is about; and • opinion holders, H, which are the entities expressing the opinion. In addition, the task involves identifying the IS- FROM and IS-ABOUT relations between an opinion expression and its holder and target, respectively. In the sample sentences, numerical subscripts in- dicate an IS-FROM or IS-ABOUT relation. S1 [The sale]T1 [infuriated]O1 [Beijing]H1,2 which [regards]O2 [Taiwan]T2 an integral part of its territory awaiting reunification, by force if necessary. S2 “[Our agency]T1,H2 [seriously needs]O2 [equipment for detecting drugs]T2,” [he]H1 [said]O1. In S1, for example, “infuriated” indicates that there is an (negative) opinion from “Beijing” re- garding “the sale.”1 Traditionally, the task of extracting opinion entities and opinion relations was handled in a pipelined manner, i.e., extracting the opinion ex- pressions first and then extracting opinion tar- gets and opinion holders based on their syntac- tic and semantic associations with the opinion ex- pressions (Kim and Hovy, 2006; Kobayashi et al., 2007). More recently, methods that jointly in- fer the opinion entity and relation extraction tasks (e.g., using Integer Linear Programming (ILP)) have been introduced (Choi et al., 2006; Yang and Cardie, 2013) and show that the existence of opin- ion relations provides clues for the identification of opinion entities and vice-versa, and thus results in better performance than a pipelined approach. However, the success of these methods depends critically on the availability of opinion …

Content truncated to 500 words