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Automatic opinion recognition involves a number of related tasks, such as identifying the boundaries of opinion expression, determining their polarity, and determining their intensity. Although much progress has been made in this area, existing research typically treats each of the above tasks in isolation. In this paper, we apply a hierarchical parameter sharing technique using Conditional Random Fields for fine-grained opinion analysis, jointly detecting the boundaries of opinion expressions as well as determining two of their key attributes — polarity and intensity. . | Hierarchical Sequential Learning for Extracting Opinions and their Attributes Yejin Choi and Claire Cardie Department of Computer Science Cornell University Ithaca NY 14853 ychoi cardie @cs.Cornell.edu Abstract Automatic opinion recognition involves a number of related tasks such as identifying the boundaries of opinion expression determining their polarity and determining their intensity. Although much progress has been made in this area existing research typically treats each of the above tasks in isolation. In this paper we apply a hierarchical parameter sharing technique using Conditional Random Fields for fine-grained opinion analysis jointly detecting the boundaries of opinion expressions as well as determining two of their key attributes polarity and intensity. Our experimental results show that our proposed approach improves the performance over a baseline that does not exploit hierarchical structure among the classes. In addition we find that the joint approach outperforms a baseline that is based on cascading two separate components. 1 Introduction Automatic opinion recognition involves a number of related tasks such as identifying expressions of opinion e.g. Kim and Hovy 2005 Popescu and Etzioni 2005 Breck et al. 2007 determining their polarity e.g. Hu and Liu 2004 Kim and Hovy 2004 Wilson et al. 2005 and determining their strength or intensity e.g. Popescu and Etzioni 2005 Wilson et al. 2006 . Most previous work treats each subtask in isolation opinion expression extraction i.e. detecting the boundaries of opinion expressions and opinion attribute classification e.g. determining values for polarity and intensity are tackled as separate steps in opinion recognition systems. Unfortunately errors from individual components will propagate in systems with cascaded component architectures causing performance degradation in the end-to-end system e.g. Finkel et al. 2006 in our case in the end-to-end opinion recognition system. In this paper we apply a .
