Question Classification using Latent Focus WordsMotivationSlide 3Question Focus Words (QFWs)Latent QFWsResultsConclusionQuestionsAcknowledgementsQuestion Classification using Latent Focus WordsPresented by:Anand IyerRitvik MudurMotivationMotivation•Question Answering systems are slowly becoming more prevalent•Web search•Yahoo answers•Forums•Question classification is an integral part of any QA system•Classify each question into a pre-defined set of classes•Fine and coarse•Limits the range of data/corpora to be searched•Makes the problem tractable (in many cases)Question Focus Words (QFWs)•Word (or phrase) indicating the main focus of the question•Excluding the interrogative words•Example•HUM:ind - Who was the first person to reach the North Pole ?•Focus word: person • Commonly found using heuristics•Templates:•‘What|Which type|kind|relative of’•Head word of first NP/VP in the question•Can also “learn” the focus word with a set of labeled “gold” QFWsLatent QFWs•We could also consider the focus words as “latent” or hidden before training•Use a gradient-descent approach to find the focus words that result in the most confident (and accurate) classifications on our training setResults•Compared to a baseline system that used only heuristics for QFW selection•Used a perceptron classifier•Begin with a single random initialization•Begin with an ensemble of 20 random initializations•Using a voting system in the first iterationConclusion•Comparable results•Many more avenues to explore•Different training approaches•Multiple QFWs•Space of latent QFWs•Hybrid approach (heuristics + latent focus words)•Exploits ambiguity•Scales easier•To more specific classes (new heuristics can be tedious to introduce)•To more data (do not require “gold” focus words)QuestionsAcknowledgementsTHANK YOUMihai Surdeanu for your guidance and
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