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Automated learning of appraisal extraction patterns Kenneth Bloom and Shlomo Argamon

Summary: Automated learning of appraisal extraction patterns
Kenneth Bloom and Shlomo Argamon
Linguistic Cognition Lab
Dept. of Computer Science
Illinois Institute of Technology
This paper describes a grammatically motivated system for extracting opinionated text. A
technique for extracting appraisal expressions has been described in previous work, using
manually constructed syntactic linkages to locate targets of the opinions. The system
extracts attitudes using a general lexicon--and some candidate targets using a domain
specific lexicon--and finds additional targets using the syntactic linkages. In this paper,
we discuss a technique for automatically learning the syntactic linkages from a list of all
extracted attitudes and the list of candidate targets. The accuracy of the new learned
linkages is comparable to the accuracy of the old manual linkages.
1. Introduction
Many traditional data mining tasks in natural language processing focus on
extracting and mining topical data. In recent years, the natural language
community has recognized the value in analyzing opinions and emotions
expressed in free text, developing the field of sentiment analysis to research
applications of opinionated text and methods for extracting it. While early


Source: Argamon, Shlomo - Department of Computer Science, Illinois Institute of Technology


Collections: Computer Technologies and Information Sciences