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The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext; Creativity and (Early) Cognitive Development; Data-Driven Wellness: From Self-Tracking to Behavior Change; Designing Intelligent Robots: Reintegrating AI II; Lifelong Machine Learning; Shikakeology: Designing Triggers for Behavior Change; Trust and Autonomous Systems; and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.
Analyzing Microtext
Much progress has been made in recent years in several areas within natural language processing. However, so far there has been less work related to microtext (for example, instant messaging, transcribed speech, and microblogs such as Twitter and Facebook). Microtext is made up of semistructured pieces of text that are distinguished by their brevity, informality, idiosyncratic lexicon, nonstandard grammar, misspelling, use of emoticons, and sometimes simultaneous interwoven conversation. These characteristics make microtext challenging to analyze. Most existing tools are trained on properly spelled and well-punctuated corpora, and therefore have problems correctly tagging and parsing microtext.
The 15 presentations focused on a broad range of microtext data sources: chat from online games, microblogs from Twitter, Facebook posts, and SMS communications. Some of the themes included creating a part of speech tagger for Twitter; sentiment extraction from tweets; gender and author detection in short noisy text; personality trait identification based on language used in social media; clustering of microtext by topic; detection of hedging and its relationship to gender, among many others. In addition to the contributed presentations and posters, the symposium included two invited talks from the leading researchers in microtext and social network analysis. Noah Smith (Carnegie Mellon University) spoke regarding the challenges and novelties of tagging and parsing microtext. His talk highlighted the need to reconsider what we call "noise" in data, for example, numerous abbreviations such as "SMH" (shake my head) and "OMG" that would be considered noise in some types of text are important "parts of speech" in Twitter and even warrant their own tag! Sofus Macskassy (Facebook) spoke about discovering Twitter users' topics of interest by examining the entities they mention in their tweets as well as various types of...