Identifying the experimental methods in human neuroimaging papers is important for grouping meaningfully similar experiments for meta-analyses. Currently, this can only be done by human readers. We present the performance of common machine learning (text mining) methods applied to the problem of automatically classifying or labeling this literature. Labeling terms are from the Cognitive Paradigm Ontology (CogPO), the text corpora are abstracts of published functional neuroimaging papers, and the methods use the performance of a human expert as training data. We aim to replicate the expert's annotation of multiple labels per abstract identifying the experimental stimuli, cognitive paradigms, response types, and other relevant dimensions of the experiments. We use several standard machine learning methods: naive Bayes (NB), k-nearest neighbor, and support vector machines (specifically SMO or sequential minimal optimization). Exact match performance ranged from only 15% in the worst cases to 78% in the best cases. NB methods combined with binary relevance transformations performed strongly and were robust to overfitting. This collection of results demonstrates what can be achieved with off-the-shelf software components and little to no pre-processing of raw text.
Abstract-Artificial conversations have many applications in chatter bot-based customer service including website navigation tools and guided online shopping. Existing approaches to generating conversations leverage linguistic and stochastic principles, where lower level grammatical and structural artifacts are modeled. These approaches perform well in pairwise utterance exchanges, but not so well in longer conversational contexts. We simulate more meaningful chatter bot conversations using an architecture that can leverage content and context. Grice's cooperative maxims, which form the central idea in the theory of pragmatics, is our framework for evaluation. The domain of our research is customer service situations, and we compare our artificial conversations with actual conversations of existing chatter bots deployed in the same domain.
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