We describe an open source web application called EntiTies for annotating the entities throughout a text and the ties between them. Example uses of this tool include extracting character networks from novels or entity networks from legal documents. EntiTies' interface allows users to annotate a text from scratch, to process it automatically, or to process it automatically and then correct it. Forking allows multiple annotations to exist for the same text. EntiTies enables texts and annotations to be easily shared with others or kept private.

Query auto completion (QAC) is nearly ubiquitous in modern search systems, however, there are few published studies on how searchers use QAC query suggestions. This study describes the use of QAC by 29 searchers working on eight assigned search topics in a lab setting. We found that our subjects had differing propensities to use QAC, with some searchers rarely using it, while others used QACs for about half their queries. This study extends prior work by examining the use of QAC in the context of whole search sessions across multiple topics, and by comparing search behavior and visual attention for queries that used QAC and those that did not. The study concludes with an exploration of differences in QAC usage and query behavior among searchers, which reveals six possible query entry profiles.

Search engines help their users decide which results to visit using captions comprising titles, URLs, and snippets containing the query keywords and proximal text from landing pages (the search results linked from the result page). Although caption content can be a key factor in these decisions, snippets provide only basic support for orienting users with landing page content from the search-engine result page (SERP), and no support during the transition to landing pages or once users reach the page following a selection decision. As a result, many searchers must employ inefficient strategies such as skimming and scanning the content of the landing page. In this paper we propose a novel method, called clickable snippets, to address this shortcoming. Clickable snippets provide searchers with a direct and actionable link between SERP captions and landing-page content. We describe a user study comparing clickable snippets with extant methods of orientation support such as query-term highlighting on the landing page and thumbnail previews on the SERP. We show that clickable snippets are preferred by participants, and lead to more effective and efficient searching. Our findings have implications for the design of the user experience in search systems.

When generating query recommendations for a user, a natural approach is to try and leverage not only the user's most recently submitted query, or reference query, but also information about the current search context, such as the user's recent search interactions. We focus on two important classes of queries that make up search contexts: those that address the same information need as the reference query (on-task queries), and those that do not (off-task queries). We analyze the effects on query recommendation performance of using contexts consisting of only on-task queries, only off-task queries, and a mix of the two. Using TREC Session Track data for simulations, we demonstrate that on-task context is helpful on average but can be easily overwhelmed when off-task queries are interleaved---a common situation according to several analyses of commercial search logs. To minimize the impact of off-task queries on recommendation performance, we consider automatic methods of identifying such queries using a state of the art search task identification technique. Our experimental results show that automatic search task identification can eliminate the effect of off-task queries in a mixed context.

With the increased availability of e-books and digitized book collections, more users are searching the web for information about books. There are many online digital libraries containing book, author and subject data, which are accessed via internal search services as well as external web sites, such as Google. Although this is a common yet complex information-seeking behavior involving multiple search systems with different characteristics, little is known about how users find information in this scenario.

No abstract available.

We describe CrowdLogging, an approach for distributed search log collection, storage, and mining, with the dual goals of preserving privacy and making the mined information broadly available. Most search log mining approaches and most privacy enhancing schemes have focused on centralized search logs and methods for disseminating them to third parties. In our approach, a user's search log is encrypted and shared in such a way that (a) the source of a search behavior artifact, such as a query, is unknown and (b) extremely rare artifacts---that is, artifacts more likely to contain private information---are not revealed. The approach works with any search behavior artifact that can be extracted from a search log, including queries, query reformulations, and query-click pairs. In this work, we: (1) present a distributed search log collection, storage, and mining framework; (2) compare several privacy policies, including differential privacy, showing the trade-offs between strong guarantees and the utility of the released data; (3) demonstrate the impact of our approach using two existing research query logs; and (4) describe a pilot study for which we implemented a version of the framework.

When search engine users have trouble finding information, they may become frustrated, possibly resulting in a bad experience (even if they are ultimately successful). In a user study in which participants were given difficult information seeking tasks, half of all queries submitted resulted in some degree of self-reported frustration. A third of all successful tasks involved at least one instance of frustration. By modeling searcher frustration, search engines can predict the current state of user frustration and decide when to intervene with alternative search strategies to prevent the user from becoming more frustrated, giving up, or switching to another search engine. We present several models to predict frustration using features extracted from query logs and physical sensors. We are able to predict frustration with a mean average precision of 65% from the physical sensors, and 87% from the query log features.

Abstract Readers of programs have two main sources of domain information: identifier names and comments. In order to efficiently maintain source code, it is important that the identifier names (as well as comments) communicate clearly the concepts they represent. Deißenböck and Pizka recently introduced two rules for creating well‐formed identifiers: one considers the consistency of identifiers and the other their conciseness. These rules require a mapping from identifiers to the concepts they represent, which may be costly to develop after the initial release of a system. An approach for verifying whether identifiers are well formed without any additional information (e.g., a concept mapping) is developed. Using a pool of 48 million lines of code, experiments with the resulting syntactic rules for well‐formed identifiers illustrate that violations of the syntactic pattern exist. Two case studies show that three‐quarters of these violations are ‘real’. That is, they could be identified using a concept mapping. Three related studies show that programmers tend to use a rather limited vocabulary, that, contrary to many other aspects of system evolution, maintenance does not introduce additional rule violations, and that open and proprietary sources differ in their percentage of violations. Copyright © 2007 John Wiley & Sons, Ltd.

The goal of this research is to apply language processing techniques to extend human judgment into situations where obtaining direct human judgment is impractical due to the volume of information that must be considered. On aspect of this is leveraged quality assessments, which can be used to evaluate third-party coded subsystems, to track quality across the versions of a program, to assess the compression effort (and subsequent cost) required to make a change, and to identify parts of a program in need of preventative maintenance. A description of the QALP tool, its output from just under two million lines of code, and an experiment aimed at evaluating the tool's use in leveraged quality assessment are presented. Statistically significant results from this experiment validate the use of the QALP tool in human leverage quality assessment

Readers of programs have two main sources of domain information: identifier names and comments. When functions are uncommented, as many are, comprehension is almost exclusively dependent on the identifier names. Assuming that writers of programs want to create quality identifiers (e.g., include relevant domain knowledge) how should they go about it? For example, do the initials of a concept name provide enough information to represent the concept? If not, and a longer identifier is needed, is an abbreviation satisfactory or does the concept need to be captured in an identifier that includes full words? Results from a study designed to investigate these questions are reported. The study involved over 100 programmers who were asked to describe twelve different functions. The functions used three different "levels" of identifiers: single letters, abbreviations, and full words. Responses allow the level of comprehension associated with the different levels to be studied. The functions include standard algorithms studied in computer science courses as well as functions extracted from production code. The results show that full word identifiers lead to the best comprehension; however, in many cases, there is no statistical difference between full words and abbreviations