@article {14415,
	title = {Organizing User Search Histories},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {24},
	year = {2012},
	month = {2012/05//},
	pages = {912 - 925},
	abstract = {Users are increasingly pursuing complex task-oriented goals on the web, such as making travel arrangements, managing finances, or planning purchases. To this end, they usually break down the tasks into a few codependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user{\textquoteright}s historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.},
	keywords = {alteration;query, analysis;user, engines;search, engines;text, goal;textual, group, historical, History, identification;query, information, interfaces;, log;sessionization;task-oriented, organization;query, processing;search, query, query;user, quest;query, ranking;search, search, search;long-term, similarity;time, suggestion;result, threshold;user, Web;collaborative},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2010.251},
	author = {Hwang,Heasoo and Lauw,H. W and Getoor, Lise and Ntoulas,A.}
}
@conference {16057,
	title = {What{\textquoteright}s being said near "Martha"? Exploring name entities in literary text collections},
	booktitle = {Visual Analytics Science and Technology, 2009. VAST 2009. IEEE Symposium on},
	year = {2009},
	month = {2009/10//},
	pages = {107 - 114},
	abstract = {A common task in literary analysis is to study characters in a novel or collection. Automatic entity extraction, text analysis and effective user interfaces facilitate character analysis. Using our interface, called POSvis, the scholar uses word clouds and self-organizing graphs to review vocabulary, to filter by part of speech, and to explore the network of characters located near characters under review. Further, visualizations show word usages within an analysis window (i.e. a book chapter), which can be compared with a reference window (i.e. the whole book). We describe the interface and report on an early case study with a humanities scholar.},
	keywords = {analysis;humanities, analysis;literary, analysis;user, clouds;word, collection;name, entity, entity;part-of-speech, extraction;character, filtering;linguistics;text, filtering;self-organizing, graph;text, interface;vocabulary;word, interfaces;vocabulary;, POSvis;automatic, scholar;literary, text, usage;data, visualisation;information},
	doi = {10.1109/VAST.2009.5333248},
	author = {Vuillemot,R. and Clement,T. and Plaisant, Catherine and Kumar,A.}
}
