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Initially, the tool is trained with a small set of manually annotated documents.
Currently, documents are placed in two folders, the 'pool' folder, where most documents are placed, and the 'gold' folder, where a smaller set of manually annotated documents is used exclusively for the evaluation of the machine learning methods' performance.
Both MutationFinder [ 6] and our OMM MutationTagger were applied to 11 manually annotated documents; the comparative results of the systems are shown in Table 5. Comparative evaluation of the mutation detection performance, using MutationFinder and OMM MutationTagger.
The final number of 451 papers consists of a test set of 20 manually annotated documents plus a training set of 431 documents, which combine manual and automatic annotations (that have subsequently been manually validated).
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We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches.
We then manually annotated the documents using a PHI schema that included all categories of PHI defined in the HIPAA "Safe Harbor" legislation [ 2], as well as some armed forces-specific information such as deployment locations, and units.
To address this task, we manually annotated reaction-document pairs with the relevance of the documents to each reaction (Section 3.1).
The performance of our system on our manually annotated corpus of 40 documents is assessed and the results are summarized in Tables 7 and 8. OMM system results are provided in an additional file [see Additional file 3].
Manual annotation: using the tagtog interface, a biocurator manually annotated a set of 20 documents with an entity class of her choice.
The task for tagtog was divided as follows: Manual annotation: using the tagtog interface, a biocurator manually annotated a set of 20 documents with an entity class of her choice.
Chemical entities were manually annotated for 40 complete patent documents and normalized to CHEBI identifiers.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com