Detecting Experiences from Weblogs

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1464–1472, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Detecting Experiences from Weblogs Keun Chan Park, Yoonjae Jeong and Sung Hyon Myaeng Department of Computer Science Korea Advanced Institute of Science and Technology {keunchan, hybris, myaeng}@kaist.ac.kr Abstract Weblogs are a source of human activity know- ledge 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 web- logs. We define experience as knowledge em- bedded in a collection of activities or events which an individual or group has actually un- dergone. Based on an observation that expe- rience-revealing sentences have a certain lin- guistic style, we formulate the problem of de- tecting experience as a classification task us- ing various features including tense, mood, as- pect, modality, experiencer, and verb classes. We also present an activity verb lexicon con- struction method based on theories of lexical semantics. Our results demonstrate that the ac- tivity verb lexicon plays a pivotal role among selected features in the classification perfor- mance and shows that our proposed method outperforms the baseline significantly. 1 Introduction In traditional philosophy, human beings are known to acquire knowledge mainly by reason- ing and experience. Reasoning allows us to draw a conclusion based on evidence, but people tend to believe it firmly when they experience or ob- serve it in the physical world. Despite the fact that direct experiences play a crucial role in mak- ing a firm decision and solving a problem, people often resort to indirect experiences by reading written materials or asking around. Among many sources people resort to, the Web has become the largest one for human expe- riences, especially with the proliferation of web- logs. While Web documents contain various types of information including facts, encyclopedic knowledge, opinions, and experiences in general, personal experiences tend to be found in weblogs more often than other web documents like news articles, home pages, and scientific papers. As such, we have begun to see some research efforts in mining experience-related attributes such as time, location, topic, and experiencer, and their relations from weblogs (Inui et al., 2008; Kura- shima et al., 2009). Mined experiences can be of practical use in wide application areas. For example, a collection of experiences from the people who visited a resort area would help planning what to do and how to do things correctly without having to spend time sifting through a variety of resources or rely on commercially-oriented sources. Another example would be a public service de- partment gleaning information about how a park is being used at a specific location and time. Experiences can be recorded around a frame like “who did what, when, where, and why” al- though opinions and emotions can be also linked. Therefore attributes such as location, time, and activity and their relations must be extracted by devising a method for selecting experience- containing sentences …

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