Cognitive Semiotic Model for Query Expansion in Question Answering
2014
Query expansion improves performance of informational retrieval stage in question answering pipeline. We state the benefits of a personalized and autonomous query preprocessing and automate a semiotic model to achieve such properties. The model operates as a context-sensitive weighted grammar, along with the algorithm to apply production rules allowing approximate matching. The semiotic model is packed into a regression model to predict relevant terms for a query. ROC-analysis evaluates the regression model and helps to choose the optimal cutoff level. We compare ranking of terms by regression model and ranking based on an external informational retrieval system.
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