Negation recognition in medical narrative reports

Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.

An analysis on document length retrieval trends in language modeling smoothing

Document length is widely recognized as an important factor for adjusting retrieval systems. Many models tend to favor the retrieval of either short or long documents and, thus, a length-based correction needs to be applied for avoiding any length bias. In Language Modeling for Information Retrieval, smoothing methods are applied to move probability mass from document terms to unseen words, which is often dependant upon document length. In this article, we perform an in-depth study of this behavior, characterized by the document length retrieval trends, of three popular smoothing methods across a number of factors, and its impact on the length of documents retrieved and retrieval performance. First, we theoretically analyze the Jelinek–Mercer, Dirichlet prior and two-stage smoothing strategies and, then, conduct an empirical analysis. In our analysis we show how Dirichlet prior smoothing caters for document length more appropriately than Jelinek–Mercer smoothing which leads to its superior retrieval performance. In a follow up analysis, we posit that length-based priors can be used to offset any bias in the length retrieval trends stemming from the retrieval formula derived by the smoothing technique. We show that the performance of Jelinek–Mercer smoothing can be significantly improved by using such a prior, which provides a natural and simple alternative to decouple the query and document modeling roles of smoothing. With the analysis of retrieval behavior conducted in this article, it is possible to understand why the Dirichlet Prior smoothing performs better than the Jelinek–Mercer, and why the performance of the Jelinek–Mercer method is improved by including a length-based prior.

Discoverability and Access in Book Publishing

Search engine discoverability programs have changed the fundamentals of book marketing by enabling content inside the book to be used as the source of marketing itself via search results inside books. The internet trifecta of search, browse, buy is the end game but the complexities of making this work is explored along with an in-depth overview of the potential upside and difficulties of online content purchase models.     

Experimentation and Innovation in U.S. Publishing Today: Findings from the Book Industry Study Group

In February 2008 the Book Industry Study Group (BISG) conducted a survey of U.S. publishers to determine the extent of technology-driven innovation and experimentation. The survey findings were published alongside a set of 10 case studies in May 2008 as “From experimentation to innovation in the digital age—case studies from the North American Book Industry 2008”. This article, written by Michael Healy, Executive Director of the BISG, summarizes the circumstances that led to the survey and shares the key findings.     

Nearest-biclusters collaborative filtering based on constant and coherent values

Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the “information overload” problem. Nearest-neighbor CF is based either on similarities between users or between items, to form a neighborhood of users or items, respectively. Recent research has tried to combine the two aforementioned approaches to improve effectiveness. Traditional clustering approaches (k-means or hierarchical clustering) has been also used to speed up the recommendation process. In this paper, we use biclustering to disclose this duality between users and items, by grouping them in both dimensions simultaneously. We propose a novel nearest-biclusters algorithm, which uses a new similarity measure that achieves partial matching of users’ preferences. We apply nearest-biclusters in combination with two different types of biclustering algorithms—Bimax and xMotif—for constant and coherent biclustering, respectively. Extensive performance evaluation results in three real-life data sets are provided, which show that the proposed method improves substantially the performance of the CF process.

Archival Science in Brazil: the establishment of a scientific discipline and its impact on training archivists in the era of information and knowledge

This article addresses the trajectory of Archival Science in Brazil, its extracurricular characteristics and its particular relationship with Information Science within academics, its institutional associations, and some considerations regarding current training for archivists in Brazil. The methodological procedures used included analyzing documents from the National Archive Fonds of Brazil, reviewing archive literature, and interviewing people involved in consolidating Archival Science. This study concludes that Archival Science research is taking place in graduate programs throughout public Brazilian universities and points out that people working with archives now have more advanced training, such as undergraduate degrees in Archival Science or other. This study also focuses on archives as information units, while presenting and reflecting on the demands of training archivists who are faced with the challenges of the working world and the era of information and knowledge—challenges that go beyond merely technical procedures and encompass dynamic planning and orientation activities.     

A compressed self-index using a Ziv–Lempel dictionary

A compressed full-text self-index for a text T, of size u, is a data structure used to search for patterns P, of size m, in T, that requires reduced space, i.e. space that depends on the empirical entropy (H _k or H ₀) of T, and is, furthermore, able to reproduce any substring of T. In this paper we present a new compressed self-index able to locate the occurrences of P in O((m + occ)log u) time, where occ is the number of occurrences. The fundamental improvement over previous LZ78 based indexes is the reduction of the search time dependency on m from O(m ²) to O(m). To achieve this result we point out the main obstacle to linear time algorithms based on LZ78 data compression and expose and explore the nature of a recurrent structure in LZ-indexes, the suffix tree. We show that our method is very competitive in practice by comparing it against other state of the art compressed indexes.

Word Play: Scaffolding Language Development Through Child-Directed Play

Play is an important activity in young children’s lives. It is how children explore their world and build knowledge. Although free play, which is play that is totally child directed, contributes to children’s learning, self-regulation and motivation, adults’ participation in children’s play is critical in their development, especially their language development. Guided by children, adults can help scaffold children’s language, and especially their learning. We suggest that adults scaffold children’s language during play by using research based strategies such as asking questions that invite extended responses and new inquiry, provide meaningful feedback and effectively use wait time, which provides children with the opportunity to respond to adults’ comments and questions. The goal is to provide adults with strategies to scaffold children’s language development during play while allowing children to direct their own play activities.     

Integrating Writing into the Early Childhood Curriculum: A Frame for Intentional and Meaningful Writing Experiences

Despite the importance of early writing development to children’s school success, research documents that early childhood teachers spend little time actively supporting children’s writing development in preschool classrooms. This article provides a framework for integrating writing experiences across the early childhood curriculum. Practical examples are given regarding how writing opportunities can be incorporated into existing activities and play settings. The metaphor of backgrounding and foregrounding writing experiences is used to illustrate ways that teachers can set writing rich environments and activities in a manner that makes it easier for teachers to bring these experiences into everyday learning opportunities. Attention is given to how teachers can bring writing to the foreground of the curriculum by drawing attention to writing materials, making natural connections with children’s interest and play, and scaffolding children’s early writing attempts and experiences.