Detecting Experiences from Weblogs
Published 11 July 2010
Keun Chan Park, Yoonjae Jeong, Sung Hyon Myaeng
Citations27
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TL;DR
This paper proposes a method for mining personal experiences from a large set of weblogs and demonstrates that the activity verb lexicon plays a pivotal role among selected features in the classification performance and shows that the proposed method outperforms the baseline significantly.
Abstract
Weblogs are a source of human activity knowledge comprising valuable information such as facts, opinions and personal experiences. In this paper, we propose a method for mining personal experiences from a large set of weblogs. We define experience as knowledge embedded in a collection of activities or events which an individual or group has actually undergone. Based on an observation that experience-revealing sentences have a certain linguistic style, we formulate the problem of detecting experience as a classification task using various features including tense, mood, aspect, modality, experiencer, and verb classes. We also present an activity verb lexicon construction method based on theories of lexical semantics. Our results demonstrate that the activity verb lexicon plays a pivotal role among selected features in the classification performance and shows that our proposed method outperforms the baseline significantly. 1
Keywords
Computer SciencePhysics and Astronomy
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