Boulos Harb

Query language modeling for voice search

The paper presents an empirical exploration of google.com query stream language modeling. We describe the normalization of the typed query stream resulting in out-of-vocabulary (OoV) rates below 1% for a one million word vocabulary. We present a comprehensive set of experiments that guided the design decisions for a voice search service. In the process we re-discovered a less known interaction between Kneser-Ney smoothing and entropy pruning, and found empirical evidence that hints at non-stationarity of the query stream, as well as strong dependence on various English locales—USA, Britain and Australia.

Near neighbor join

An increasing number of Web applications such as friends recommendation depend on the ability to join objects at scale. The traditional approach taken is nearest neighbor join (also called similarity join), whose goal is to find, based on a given join function, the closest set of objects or all the objects within a distance threshold to each object in the input. The scalability of techniques utilizing this approach often depends on the characteristics of the objects and the join function. However, many real-world join functions are intricately engineered and constantly evolving, which makes the design of white-box methods that rely on understanding the join function impractical. Finding a technique that can join extremely large number of objects with complex join functions has always been a tough challenge. In this paper, we propose a practical alternative approach called near neighbor join that, although does not find the closest neighbors, finds close neighbors, and can do so at extremely large scale when the join functions are complex. In particular, we design and implement a super-scalable system we name SAJ that is capable of best-effort joining of billions of objects for complex functions. Extensive experimental analysis over real-world large datasets shows that SAJ is scalable and generates good results.

Recent progress towards an ecosystem of structured data on the Web

Google Fusion Tables aims to support an ecosystem of structured data on the Web by providing a tool for managing and visualizing data on the one hand, and for searching and exploring for data on the other. This paper describes a few recent developments in our efforts to further the ecosystem.

Mobile music modeling, analysis and recognition

We present an analysis of music modeling and recognition techniques in the context of mobile music matching, substantially improving on the techniques presented in [1]. We accomplish this by adapting the features specifically to this task, and by introducing new modeling techniques that enable using a corpus of noisy and channel-distorted data to improve mobile music recognition quality. We report the results of an extensive empirical investigation of the systems robustness under realistic channel effects and distortions. We show an improvement of recognition accuracy by explicit duration modeling of music phonemes and by integrating the expected noise environment into the training process. Finally, we propose the use of frame-to-phoneme alignment for high-level structure analysis of polyphonic music.