| Semantic and syntactic features found in text can be used in combination to statistically predict linguistic devices such as hedges in online chat. Some features are better indicators than others, and there are cases when multiple features need to be considered together to be useful. Once the features are identified, it becomes an optimization problem to find the best division of data. We have devised a genetic algorithm approach towards detecting hedges in online multi-party chat discourse. A system was created using rewards and penalties for matching features in tokenized text, so optimizing the reward and penalty amounts are the main challenge. Genetic algorithms, a subset of Evolutionary Algorithms, are great for optimization; as they are massively parallel directed searches, and therefore suited to finding the best ratio of integer rewards and penalties. "Evolutionary algorithms (EAs) utilize principles of natural selection and are robust adaptive search schemes suitable for searching nonlinear, discontinuous, and high-dimensional spaces. This class of algorithms is being increasingly applied to obtain optimal or near-optimal solutions to many complex real-world optimization problems" (Bonissone, et. al. 2006) We show results using 10-fold cross validation as commonly used in traditional machine learning. The best performance without further fine tuning is 79% in classifying whether an utterance in chat contains a hedge or not. |
