Short Messaging Service (SMS) is popularly used to provide information access to people on the move. This has resulted in the growth of SMS based Question Answering (QA) services. However automatically handling SMS questions poses significant challenges due to the inherent noise in SMS questions. In this work we present an automatic FAQ-based question answering system for SMS users. We handle the noise in a SMS query by formulating the query similarity over FAQ questions as a combinatorial search problem. The search space consists of combinations of all possible dictionary variations of tokens in the noisy query. We present an efficient search algorithm that does not require any training data or SMS normalization and can handle semantic variations in question formulation. We demonstrate the effectiveness of our approach on two reallife datasets.

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Address Cleansing is very challenging, particularly for geographies with variability in writing addresses. Supervised learners can be easily trained for different data sources. However, training requires labeling large corpora for each data source which is time consuming and labor intensive to create. We propose a method to automatically transfer supervision from a given labeled source to a target unlabeled source using a hierarchical dirichlet process. Each dirichlet process models data from one source. The shared component distribution across these dirichlet processes captures the semantic relation between data sources. A feature projection on the component distributions from multiple sources is used to transfer supervision.