Marking up search queries with linguistic annotations such as part-of-speech tags, capitalization, and segmentation, is an important part of query processing and understanding in information retrieval systems. Due to their brevity and idiosyncratic structure, search queries pose a challenge to existing NLP tools. To address this challenge, we propose a probabilistic approach for performing joint query annotation. First, we derive a robust set of unsupervised independent annotations, using queries and pseudo-relevance feedback. Then, we stack additional classifiers on the independent annotations, and exploit the dependencies between them to further improve the accuracy, even with a very limited amount of available training data. We evaluate our method using a range of queries extracted from a web search log. Experimental results verify the effectiveness of our approach for both short keyword queries, and verbose natural language queries. 1

This paper introduces the task of Evidence Finding, a novel information retrieval task that uses books—a traditionally more trust-worthy source of information—to help provide evidence to supportastatement. What makes this evidencefinding task different from other tasks, such as the related INEX Prove It task, is that both the statement for which evidence is sought and its context are given to the search system. A practical application of this system is to provide supporting or refuting evidence from books for a statement made within a Wikipedia article, using the entire article as contextual support for query generation. We provide details of this task as well as an analysis of a number of retrieval methods that address this task. Categories andSubjectDescriptors H.3.3[Information Storage and Retrieval]: Information