tailieunhanh - Báo cáo khoa học: "Extracting Opinion Expressions and Their Polarities – Exploration of Pipelines and Joint Models"

We investigate systems that identify opinion expressions and assigns polarities to the extracted expressions. In particular, we demonstrate the benefit of integrating opinion extraction and polarity classification into a joint model using features reflecting the global polarity structure. The model is trained using large-margin structured prediction methods. | Extracting Opinion Expressions and Their Polarities - Exploration of Pipelines and Joint Models Richard Johansson and Alessandro Moschitti DISI University of Trento Via Sommarive 14 38123 Trento TN Italy johansson moschitti @ Abstract We investigate systems that identify opinion expressions and assigns polarities to the extracted expressions. In particular we demonstrate the benefit of integrating opinion extraction and polarity classification into a joint model using features reflecting the global polarity structure. The model is trained using large-margin structured prediction methods. The system is evaluated on the MPQA opinion corpus where we compare it to the only previously published end-to-end system for opinion expression extraction and polarity classification. The results show an improvement of between 10 and 15 absolute points in F-measure. 1 Introduction Automatic systems for the analysis of opinions expressed in text on the web have been studied extensively. Initially this was formulated as a coarsegrained task - locating opinionated documents -and tackled using methods derived from standard retrieval or categorization. However in recent years there has been a shift towards a more detailed task not only finding the text expressing the opinion but also analysing it who holds the opinion and to what is addressed it is positive or negative polarity what its intensity is. This more complex formulation leads us deep into NLP territory the methods employed here have been inspired by information extraction and semantic role labeling combinatorial optimization and structured machine learning. A crucial step in the automatic analysis of opinion is to mark up the opinion expressions the pieces of 101 text allowing us to infer that someone has a particular feeling about some topic. Then opinions can be assigned a polarity describing whether the feeling is positive neutral or negative. These two tasks have generally been tackled in isolation. Breck et