As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results. Errors introduced by automatic speech recognition (ASR) lead to irrelevant search results returned to the user, thus causing user dissatisfaction. In this paper, we introduce an approach, "Mondegreen", to correct voice queries in text space without depending on audio signals, which may not always be available due to system constraints or privacy or bandwidth (for example, some ASR systems run on-device) considerations. We focus on voice queries transcribed via several proprietary commercial ASR systems. These queries come from users making internet, or online service search queries. We first present an analysis showing how different the language distribution coming from user voice queries is from that in traditional text corpora used to train off-the-shelf ASR systems. We then demonstrate that Mondegreen can achieve significant improvements in increased user interaction by correcting user voice queries in one of the largest search systems in Google. Finally, we see Mondegreen as complementing existing highly-optimized production ASR systems, which may not be frequently retrained and thus lag behind due to vocabulary drifts.

We study the localization prediction of membrane proteins for two families of medically important disease-causing bacteria, called gram-negative and gram-positive bacteria. Each such bacterium has its cell surrounded by several layers of membranes. Identifying where proteins are located in a bacterial cell is of primary research interest for antibiotic and vaccine drug design. This problem has three requirements: First, with any subsequence of amino acid residues being potentially a dimension, it has an extremely high dimensionality, few being irrelevant. Second, the prediction of a target localization site must have a high precision in order to be useful to biologists, i.e., at least 90 percent or even 95 percent, while recall is as high as possible. Achieving such a precision is made harder by the fact that target sequences are often much fewer than background sequences. Third, the rationale of prediction should be understandable to biologists for taking actions. Meeting all these requirements presents a significant challenge in that a high dimensionality requires a complex model that is often hard to understand. The support vector machine (SVM) model has an outstanding performance in a high-dimensional space, therefore, it addresses the first two requirements. However, the SVM model involves many features in a single kernel function, therefore, it does not address the third requirement. We address all three requirements by integrating the SVM model with a rule-based model, where the understandable if-then rules capture "major structures" and the elaborated SVM model captures "subtle structures". Importantly, the integrated model preserves the precision/ recall performance of SVM and, at the same time, exposes major structures in a form understandable to the human user. We focus on searching for high quality rules and partitioning the prediction between rules and SVM so as to achieve these properties. We evaluate our method on several membrane localization problems. The purpose of this paper is not improving the precision/recall of SVM, but is manifesting the rationale of a SVM classifier through partitioning the classification between if-then rules and the SVM classifier and preserving the precision/recall of SVM.

Direct marketing refers to a process of identifying and mailing to potential customers. We push the customer value as the first class information. Our approach is to estimate directly the profit generated on a customer without estimating the conditional class probability. This methodology opens up new possibilities for profit estimation. We use association rules to summarize customer groups and to build a model for profit prediction. The advantage of the association rule approach is its scalability of finding correlated features that may never be found in local search. The evaluation on the well known, large and challenging KDD-CUP-98 task shows a breakthrough result.

We study the problem of mining changes of classification characteristics as the data changes. Available are an old classifier, representing previous knowledge about classification characteristics, and a new data. We want to find the changes of classification characteristics in the new data. An example of such changes is “members with a large family no longer shop frequently, but they used to”. Finding this kind of changes holds the key for the organization to adopt to the changed environment and stay ahead of competitors. The challenge is that it is difficult to see what has really changed from comparing the old and new classifiers that could be very large and different. In this paper, we propose a technique to identify such changes. The idea is tracing the characteristics, in the old and new classifiers, that correspond to each other by classifying the same examples. We describe several ways to present changes so that the user can focus on a small number of important ones. We evaluate the proposed method on real life data sets.

1 Introduction Two features have successfully made on-line information comprehensible and accessible to people: hierarchically structured classes where topics are organized into a hierarchy of increasing specificity, and multi-classed documents where a document is classified into all relevant classes. One such information source is Yahoo! where a document on Dance, for example, could be reached from both Arts: Performing_Arts and Recreation topics in the topic hierarchy. The hierarchical feature of classes allows information to be examined and browsed at various topic specificities, and the multi-class feature allows information to be accessed from all related topics. However, most document classification techniques assume that there is a flat class space and each document has one class. The documents classified by such techniques are difficult to browse and access by people, especially when there are many classes such as in Yahoo!. In this paper, we propose a new technique for automatic classification of documents to address these real life requirements. This raises several research issues. We use Yahoo! for explanation.

this paper, we answer these questions and propose a general method for turning an arbitrary set of rules, in particular, association rules, into a classifier. In the past few years, many association rules and mining algorithms were proposed. However, the user often faces difficulties in making sense out of association rules. Indeed, no indication is given by association rules as to whether a specific but more confident rule or a general but less confident rule should be used to recommend products to new customers, and what hit rate a set of association rules will result in. Knowing such information is extremely important in a business decision making. Addressing such issues is the topic of this paper.