This paper presents GATE Teamware-an open-source, web-based, collaborative text annotation framework. It enables users to carry out complex corpus annotation projects, involving distributed annotator teams. Different user roles are provided (annotator, manager, administrator) with customisable user interface functionalities, in order to support the complex workflows and user interactions that occur in corpus annotation projects. Documents may be pre-processed automatically, so that human annotators can begin with text that has already been pre-annotated and thus making them more efficient. The user interface is simple to learn, aimed at non-experts, and runs in an ordinary web browser, without need of additional software installation. GATE Teamware has been evaluated through the creation of several gold standard corpora and internal projects, as well as through external evaluation in commercial and EU text annotation projects. It is
Using semantic technologies for mining and intelligent information access to microblogs is a challenging, emerging research area. Unlike carefully authored news text and other longer content, tweets pose a number of new challenges, due to their short, noisy, contextdependent, and dynamic nature. Semantic annotation of tweets is typically performed in a pipeline, comprising successive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). Consequently, errors are cumulative, and earlier-stage problems can severely reduce the performance of final stages. This paper presents a characterisation of genre-specific problems at each semantic annotation stage and the impact on subsequent stages. Critically, we evaluate impact on two high-level semantic annotation tasks: named entity detection and disambiguation. Our results demonstrate the importance of making approaches specific to the genre, and indicate a diminishing returns effect that reduces the effectiveness of complex text normalisation.
When researching new product ideas or filing new patents, inventors need to retrieve all relevant pre-existing know-how and/or to exploit and enforce patents in their technological domain. However, this process is hindered by lack of richer metadata, which if present, would allow more powerful concept-based search to complement the current keywordbased approach. This paper presents our approach to automatic patent enrichment, tested in large-scale, parallel experiments on USPTO and EPO documents. It starts by defining the metadata annotation task and examines its challenges. The text analysis tools are presented next, including details on automatic annotation of sections, references and measurements. The key challenges encountered were dealing with ambiguities and errors in the data; creation and maintenance of large, domain-independent dictionaries; and building an efficient, robust patent analysis pipeline, capable of dealing with terabytes of data. The accuracy of automatically created metadata is evaluated against a human-annotated gold standard, with results of over 90% on most annotation types.
Abstract. Instance unification determines whether two instances in an ontology refer to the same object in the real world. More specifically, this paper addresses the instance unification problem for person names. The approach combines the use of citation information (i.e., abstract, initials, titles and co-authorship information) with web mining, in order to gather additional evidence for the instance unification algorithm. The method is evaluated on two datasets -one from the BT digital library and one used in previous work on name disambiguation. The results show that the information mined from the web contributes substantially towards the successful handling of highly ambiguous cases which lowered the performance of previous methods.
BackgroundGenome-wide association studies (GWAS) require large sample sizes to obtain adequate statistical power, but it may be possible to increase the power by incorporating complementary data. In this study we investigated the feasibility of automatically retrieving information from the medical literature and leveraging this information in GWAS.MethodsWe developed a method that searches through PubMed abstracts for pre-assigned keywords and key concepts, and uses this information to assign prior probabilities of association for each single nucleotide polymorphism (SNP) with the phenotype of interest - the Adjusting Association Priors with Text (AdAPT) method. Association results from a GWAS can subsequently be ranked in the context of these priors using the Bayes False Discovery Probability (BFDP) framework. We initially tested AdAPT by comparing rankings of known susceptibility alleles in a previous lung cancer GWAS, and subsequently applied it in a two-phase GWAS of oral cancer.ResultsKnown lung cancer susceptibility SNPs were consistently ranked higher by AdAPT BFDPs than by p-values. In the oral cancer GWAS, we sought to replicate the top five SNPs as ranked by AdAPT BFDPs, of which rs991316, located in the ADH gene region of 4q23, displayed a statistically significant association with oral cancer risk in the replication phase (per-rare-allele log additive p-value [ptrend] = 2.5×10−3). The combined OR for having one additional rare allele was 0.83 (95% CI: 0.76–0.90), and this association was independent of previously identified susceptibility SNPs that are associated with overall UADT cancer in this gene region. We also investigated if rs991316 was associated with other cancers of the upper aerodigestive tract (UADT), but no additional association signal was found.ConclusionThis study highlights the potential utility of systematically incorporating prior knowledge from the medical literature in genome-wide analyses using the AdAPT methodology. AdAPT is available online (url: http://services.gate.ac.uk/lld/gwas/service/config).
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