The volume of published biomedical research, and therefore the underlying biomedical knowledge base, is expanding at an increasing rate. Among the tools that can aid researchers in coping with this information overload are text mining and knowledge extraction. Significant progress has been made in applying text mining to named entity recognition, text classification, terminology extraction, relationship extraction and hypothesis generation. Several research groups are constructing integrated flexible text-mining systems intended for multiple uses. The major challenge of biomedical text mining over the next 5–10 years is to make these systems useful to biomedical researchers. This will require enhanced access to full text, better understanding of the feature space of biomedical literature, better methods for measuring the usefulness of systems to users, and continued cooperation with the biomedical research community to ensure that their needs are addressed.

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A survey of current work in biomedical text mining

Usability testing of software applications developed for mobile devices is an emerging research area that faces a variety of challenges due to unique features of mobile devices, limited bandwidth, unreliability of wireless networks, as well as changing context (environmental factors) Traditional guidelines and methods used in usability testing of desktop applications may not be directly applicable to a mobile environment Therefore, it is essential to develop and adopt appropriate research methodologies that can evaluate the usability of mobile applications The contribution of this article is to propose a generic framework for conducting usability tests for mobile applications through discussing research questions, methodologies, and usability attributes The article provides an overview of existing mobil application usability studies and discusses major research questions that have been investigated Then, it proposes a generic framework and provides detailed guidelines on how to conduct such usability

Challenges, Methodologies, and Issues in the Usability Testing of Mobile Applications

We describe Web-a-Where, a system for associating geography with Web pages. Web-a-Where locates mentions of places and determines the place each name refers to. In addition, it assigns to each page a geographic focus --- a locality that the page discusses as a whole. The tagging process is simple and fast, aimed to be applied to large collections of Web pages and to facilitate a variety of location-based applications and data analyses.Geotagging involves arbitrating two types of ambiguities: geo/non-geo and geo/geo. A geo/non-geo ambiguity occurs when a place name also has a non-geographic meaning, such as a person name (e.g., Berlin) or a common word (Turkey). Geo/geo ambiguity arises when distinct places have the same name, as in London, England vs. London, Ontario.An implementation of the tagger within the framework of the WebFountain data mining system is described, and evaluated on several corpora of real Web pages. Precision of up to 82% on individual geotags is achieved. We also evaluate the relative contribution of various heuristics the tagger employs, and evaluate the focus-finding algorithm using a corpus pretagged with localities, showing that as many as 91% of the foci reported are correct up to the country level.

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Web-a-where: geotagging web content

This paper provides overview and instruction regarding the evaluation of interactive information retrieval systems with users The primary goal of this article is to catalog and compile material related to this topic into a single source This article (1) provides historical background on the development of user-centered approaches to the evaluation of interactive information retrieval systems; (2) describes the major components of interactive information retrieval system evaluation; (3) describes different experimental designs and sampling strategies; (4) presents core instruments and data collection techniques and measures; (5) explains basic data analysis techniques; and (4) reviews and discusses previous studies This article also discusses validity and reliability issues with respect to both measures and methods, presents background information on research ethics and discusses some ethical issues which are specific to studies of interactive information retrieval (IIR) Finally, this article concludes with a discussion of outstanding challenges and future research directions

https://www.nowpublishers.com/article/DownloadSummary/INR-012

Methods for Evaluating Interactive Information Retrieval Systems with Users

Lately, there has been a great interest in the application of information extraction methods to the biomedical domain, in particular, to the extraction of relationships of genes, proteins, and RNA from scientific publications. The development and evaluation of such methods requires annotated domain corpora. We present BioInfer (Bio Information Extraction Resource), a new public resource providing an annotated corpus of biomedical English. We describe an annotation scheme capturing named entities and their relationships along with a dependency analysis of sentence syntax. We further present ontologies defining the types of entities and relationships annotated in the corpus. Currently, the corpus contains 1100 sentences from abstracts of biomedical research articles annotated for relationships, named entities, as well as syntactic dependencies. Supporting software is provided with the corpus. The corpus is unique in the domain in combining these annotation types for a single set of sentences, and in the level of detail of the relationship annotation. We introduce a corpus targeted at protein, gene, and RNA relationships which serves as a resource for the development of information extraction systems and their components such as parsers and domain analyzers. The corpus will be maintained and further developed with a current version being available at http://www.it.utu.fi/BioInfer
 .

/pdf/bioinfer-a-corpus-for-information-extraction-in-the-3a18wphbxu.pdf

BioInfer: a corpus for information extraction in the biomedical domain

We describe WEST, a WEb browser for Small Terminals, that aims to solve some of the problems associated with accessing web pages on hand-held devices. Through a novel combination of text reduction and focus+context visualization, users can access web pages from a very limited display environment, since the system will provide an overview of the contents of a web page even when it is too large to be displayed in its entirety. To make maximum use of the limited resources available on a typical hand-held terminal, much of the most demanding work is done by a proxy server, allowing the terminal to concentrate on the task of providing responsive user interaction. The system makes use of some interaction concepts reminiscent of those defined in the Wireless Application Protocol (WAP), making it possible to utilize the techniques described here for WAP-compliant devices and services that may become available in the near future.

/pdf/west-a-web-browser-for-small-terminals-3j5cafrssc.pdf

WEST: a Web browser for small terminals

Protein names and how to find them.

This paper introduces four different notions of correctness to be used when measuring the performance of protein name taggers, each of which reflects certain characteristics of the tagger under evaluation. The discussion regarding the different notions is centered around the evaluation of two protein name taggers; Yapex, developed by the authors, and KeX developed by Fukuda et al. (1998). For the purpose of illustrating the difference between the ways of evaluation, both taggers are applied to a test corpus of 101 MEDLINE abstracts in which all occurrences of protein names have been marked up by domain experts.

Notions of correctness when evaluating protein name taggers

Users pose very short queries to information retrieval systems. This study shows that the apparent length of the query field has an effect on the length of the query users enter.

Verbosity and Interface Design

This paper presents work on a method to detect names of proteins in running text.

Our system - Yapex - uses a combination of lexical and syntactic knowledge, heuristic filters and a local dynamic dictionary. The syntactic information given by a general-purpose off-the-shelf parser supports the correct identification of the boundaries of protein names, and the local dynamic dictionary finds protein names in positions incompletely analysed by the parser.

We present the different steps involved in our approach to protein tagging, and show how combinations of them influence recall and precision.

We evaluate the system on a corpus of MEDLINE abstracts and compare it with the KeX system (Fukuda et al., 1998) along four different notions of correctness.

Kristofer Franzén

Papers

WEST: a Web browser for small terminals

Protein names and how to find them.

Notions of correctness when evaluating protein name taggers

Verbosity and Interface Design

Exploiting Syntax when Detecting Protein Names in Text