We present a probabilistic model of a user's search history and a target query reformulation. We derive a simple transitive similarity algorithm for disambiguating queries and improving history-based query reformulation accuracy. We compare the merits of this approach to other methods and present results on both examples assessed by human editors and on automatically-labeled click data.

We present a document expansion approach that uses Conditional Random Field (CRF) segmentation to automatically extract salient phrases from ad titles. We then supplement the ad document with query segments that are probable translations of the document phrases, as learned from a large commercial search engine's click logs. Our approach provides a significant improvement in DCG and interpolated precision and recall on a large set of human labeled query-ad pairs.

We describe a machine learning approach for predicting sponsored search ad relevance. Our baseline model incorporates basic features of text overlap and we then extend the model to learn from past user clicks on advertisements. We present a novel approach using translation models to learn user click propensity from sparse click logs.

Discovering users' specific and implicit geographic intention in web search can greatly help satisfy users' information needs. We build a geo intent analysis system that uses minimal supervision to learn a model from large amounts of web-search logs for this discovery. We build a city language model, which is a probabilistic representation of the language surrounding the mention of a city in web queries. We use several features derived from these language models to: (1) identify users' implicit geo intent and pinpoint the city corresponding to this intent, (2) determine whether the geo-intent is localized around the users' current geographic location, (3) predict cities for queries that have a mention of an entity that is located in a specific place. Experimental results demonstrate the effectiveness of using features derived from the city language model. We find that (1) the system has over 90% precision and more than 74% accuracy for the task of detecting users' implicit city level geo intent (2) the system achieves more than 96% accuracy in determining whether implicit geo queries are local geo queries, neighbor region geo queries or none-of-these (3) the city language model can effectively retrieve cities in location-specific queries with high precision (88%) and recall (74%); human evaluation shows that the language model predicts city labels for location-specific queries with high accuracy (84.5%).

This paper describes recent work on the HTK large vocabulary speech recognition system. The system uses tied-state cross-word context-dependent mixture Gaussian HMMs and a dynamic network decoder that can operate in a single pass. In the last year the decoder has been extended to produce word lattices to allow flexible and efficient system development, as well as multi-pass operation for use with computationally expensive acoustic and/or language models. The system vocabulary can now be up to 65 k words, the final acoustic models have been extended to be sensitive to more acoustic context (quinphones), a 4-gram language model has been used and unsupervised incremental speaker adaptation incorporated. The resulting system gave the lowest error rates on both the H1-P0 and H1-C1 hub tasks in the November 1994 ARPA CSR evaluation.