A Progressive Visual Analytics Tool for Incremental Experimental Evaluation

This paper presents a visual tool, AVIATOR, that integrates the progressive visual analytics paradigm in the IR evaluation process. This tool serves to speed-up and facilitate the performance assessment of retrieval models enabling a result analysis through visual facilities. AVIATOR goes one step beyond the common "compute wait visualize" analytics paradigm, introducing a continuous evaluation mechanism that minimizes human and computational resource consumption

Critical Assessment of Information Extraction Systems in Biology

An increasing number of groups are now working in the area of text mining, focusing on a wide range of problems and applying both statistical and linguistic approaches. However, it is not possible to compare the different approaches, because there are no common standards or evaluation criteria; in addition, the various groups are addressing different problems, often using private datasets. As a result, it is impossible to determine how well the existing systems perform, and particularly what performance level can be expected in real applications. This is similar to the situation in text processing in the late 1980s, prior to the Message Understanding Conferences (MUCs). With the introduction of a common evaluation and standardized evaluation metrics as part of these conferences, it became possible to compare approaches, to identify those techniques that did or did not work and to make progress. This progress has resulted in a common pipeline of processes and a set of shared tools available to the general research community. The field of biology is ripe for a similar experiment. Inspired by this example, the BioLINK group (Biological Literature, Information and Knowledge [1]) is organizing a CASP-like evaluation for the text data-mining community applied to biology. The two main tasks specifically address two major bottlenecks for text mining in biology: (1) the correct detection of gene and protein names in text; and (2) the extraction of functional information related to proteins based on the GO classification system. For further information and participation details, see http://www.pdg.cnb.uam.es/BioLink/BioCreative.eval.htm

Academic competitions

Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed

Validation of an improved computer-assisted technique for mining free-text electronic medical records

Background: The use of electronic medical records (EMRs) offers opportunity for clinical epidemiological research. With large EMR databases, automated analysis processes are necessary but require thorough validation before they can be routinely used. Objective: The aim of this study was to validate a computer-assisted technique using commercially available content analysis software (SimStat-WordStat v.6 (SS/WS), Provalis Research) for mining free-text EMRs. Methods: The dataset used for the validation process included life-long EMRs from 335 patients (17,563 rows of data), selected at random from a larger dataset (141,543 patients, ~2.6 million rows of data) and obtained from 10 equine veterinary practices in the United Kingdom. The ability of the computer-assisted technique to detect rows of data (cases) of colic, renal failure, right dorsal colitis, and non-steroidal anti-inflammatory drug (NSAID) use in the population was compared with manual classification. The first step of the computer-assisted analysis process was the definition of inclusion dictionaries to identify cases, including terms identifying a condition of interest. Words in inclusion dictionaries were selected from the list of all words in the dataset obtained in SS/WS. The second step consisted of defining an exclusion dictionary, including combinations of words to remove cases erroneously classified by the inclusion dictionary alone. The third step was the definition of a reinclusion dictionary to reinclude cases that had been erroneously classified by the exclusion dictionary. Finally, cases obtained by the exclusion dictionary were removed from cases obtained by the inclusion dictionary, and cases from the reinclusion dictionary were subsequently reincluded using Rv3.0.2 (R Foundation for Statistical Computing, Vienna, Austria). Manual analysis was performed as a separate process by a single experienced clinician reading through the dataset once and classifying each row of data based on the interpretation of the free-text notes. Validation was performed by comparison of the computer-assisted method with manual analysis, which was used as the gold standard. Sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and F values of the computer-assisted process were calculated by comparing them with the manual classification. Results: Lowest sensitivity, specificity, PPVs, NPVs, and F values were 99.82% (1128/1130), 99.88% (16410/16429), 94.6% (223/239), 100.00% (16410/16412), and 99.0% (100×2×0.983×0.998/[0.983+0.998]), respectively. The computer-assisted process required few seconds to run, although an estimated 30 h were required for dictionary creation. Manual classification required approximately 80 man-hours. Conclusions: The critical step in this work is the creation of accurate and inclusive dictionaries to ensure that no potential cases are missed. It is significantly easier to remove false positive terms from a SS/WS selected subset of a large database than search that original database for potential false negatives. The benefits of using this method are proportional to the size of the dataset to be analyzed

Survey and evaluation of query intent detection methods

Second ACM International Conference on Web Search and Data Mining, Barcelona (Spain)User interactions with search engines reveal three main underlying intents, namely navigational, informational, and transactional. By providing more accurate results depending on such query intents the performance of search engines can be greatly improved. Therefore, query classification has been an active research topic for the last years. However, while query topic classification has deserved a specific bakeoff, no evaluation campaign has been devoted to the study of automatic query intent detection. In this paper some of the available query intent detection techniques are reviewed, an evaluation framework is proposed, and it is used to compare those methods in order to shed light on their relative performance and drawbacks. As it will be shown, manually prepared gold-standard files are much needed, and traditional pooling is not the most feasible evaluation method. In addition to this, future lines of work in both query intent detection and its evaluation are propose

PageRank without hyperlinks: Reranking with PubMed related article networks for biomedical text retrieval

Graph analysis algorithms such as PageRank and HITS have been successful in Web environments because they are able to extract important inter-document relationships from manually-created hyperlinks. We consider the application of these algorithms to related document networks comprised of automatically-generated content-similarity links. Specifically, this work tackles the problem of document retrieval in the biomedical domain, in the context of the PubMed search engine. A series of reranking experiments demonstrate that incorporating evidence extracted from link structure yields significant improvements in terms of standard ranked retrieval metrics. These results extend the applicability of link analysis algorithms to different environments

PubMed related articles: a probabilistic topic-based model for content similarity

<p>Abstract</p> <p>Background</p> <p>We present a probabilistic topic-based model for content similarity called <it>pmra </it>that underlies the related article search feature in PubMed. Whether or not a document is about a particular topic is computed from term frequencies, modeled as Poisson distributions. Unlike previous probabilistic retrieval models, we do not attempt to estimate relevance–but rather our focus is "relatedness", the probability that a user would want to examine a particular document given known interest in another. We also describe a novel technique for estimating parameters that does not require human relevance judgments; instead, the process is based on the existence of MeSH ^®in MEDLINE ^®.</p> <p>Results</p> <p>The <it>pmra </it>retrieval model was compared against <it>bm25</it>, a competitive probabilistic model that shares theoretical similarities. Experiments using the test collection from the TREC 2005 genomics track shows a small but statistically significant improvement of <it>pmra </it>over <it>bm25 </it>in terms of precision.</p> <p>Conclusion</p> <p>Our experiments suggest that the <it>pmra </it>model provides an effective ranking algorithm for related article search.</p