By Dekang Lin (auth.), Atefeh Farzindar, Vlado Kešelj (eds.)
This booklet constitutes the refereed lawsuits of the twenty third convention on man made Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers awarded including 26 revised brief papers, 12 papers from the graduate scholar symposium and the abstracts of three keynote shows have been conscientiously reviewed and chosen from ninety submissions. The papers are geared up in topical sections on textual content type; textual content summarization and IR; reasoning and e-commerce; probabilistic computing device studying; neural networks and swarm optimization; desktop studying and information mining; usual language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; laptop studying; and information mining.
Read or Download Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings PDF
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Extra info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 – June 2, 2010. Proceedings
This step prevents the system from coming up with a lot of useless tokens as features for our first-level classifier. Offensive Language Detection Using Multi-level Classification 21 probability of being a flame for each context. At this level we make a pattern for each of the entries that match a variety of word sequences (Replacing Somebody or Something wild cards for the above example). In this way each pattern could be matched with any sequence of words in which we have a few (not more than three) tokens in place of wild cards.
Razavi et al. However, computer software does not have the ability of capturing the exact concept of a flame context; yet, there are some useful features that we could point out, such as: ─ The frequency of phrases which fall into one of the graded (weighted) flaming patterns (for each grade/weight separately); ─ The frequency of graded/weighted words or phrases with abusive/extremist load, in each grade; ─ The highest grade (maximum weight) which occurs in a context; ─ The normalized average of the graded/weighted words or phrases.
It also could be useful over text chat services, as well as any comment acceptance posts in social networking sites like Orkut and Facebook. In future work, we could apply second order co-occurrence features (Pedersen et. al. ) in order to extract more semantic information by processing surrounding terms and contexts of each preliminarily detected flame. We could add a synchronized adaptive weight modifier module to the IALD accessory, based on further provided training data. Acknowledgements.