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For each of 248 cases, a set of relevant reference data (as sentences or key phrases), with indicated importance by numeric weights (or other comparable meanings), to be used for quality verification.

NLP-extracted, DSD-enriched result set and graph of 248 cases (from M2, Component 2).

Iteratively incorporate the DSD into the NLP process to get a more granular, supervised result set and graph.Ī.2.3 Milestone 3: Scoring and refining of process outputs.

These results will comprise the ‘Domain Specific Datasets’ (DSD).

With domain experts to identify which key phrases identified in M1 are relevant, relationships between them as well as other external ‘enrichment’ datasets that can be used to complement the M1 results.

Recommendations for standardizing, collecting, and maintaining a set of DSDs for this project and later use (for both scientists and domain-experts).

NLP-extracted, DSD-enriched result set and graph of 250 cases (for M3, Component 1),

A number of ‘Domain Specific Datasets’ (DSD), each contains a sets of domain-specific key phrases, relationships between them (if any), relevancy weights (if applicable), or external ‘enrichment’ datasets that can be used to complement the structured text corpora (from M1, Component 1),.

Improperly classified or extracted content as input back for improving DSD (from M3, Component 1).

Structured text corpora built based on extracted content of 248 cases (from M1, Component 1).

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Filter non-standard documents and update assumptions about relationships.Ī.2.2 Milestone 2: Supervised learning using domain expertise

Conduct preliminary document processing (DP) on assembled documents according to identified standard document structure, content formats and high-level logical relations between documents and their content structure.

Advise on appropriate mode for making the documents associated with 248 cases accessible, as well as requirements for identifying assumptions about document structures and relationships.

Documented discrepancies versus preliminary assumptions (for domain experts to adjust content of the 248 cases when repeating M1, Component 1).

Structured text corpora built based on extracted content of 248 cases (for M2, Component 2),Ĭustomizable configuration per document format for processing assistance (for later, related use by DP in M1, Component 1),.

250 cases with all accompanied documents, descriptions, and notes (if any).

A.2 Scope A.2.1 Milestone 1: Document preparation and processing for NLP analysis The purpose of this contract is to conduct a supervised learning exercise to make raw NLP extractions more relevant to type of content that is typically sought from PATH documents. However, the organization of these ideas, and application of the machine learning used in previous projects requires a supervised learning component in order to apply the analysis to future datasets.

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It is concluded that there are opportunities to gain better insights, reduce manual repetitive work, increase performance and accuracy by application of automation, machine learning, and graph network science.Īs a continuation of this work, DFO has conducted Natural Language Processing on a set of 248 PATH files, in order to extract key ideas. The study briefly analyzed current working process, existing data for machine learning, objective of work authorizations.

The Project A.1 BackgroundĪ feasibility study was recently completed to evaluate automation of the application of domain knowledge for current working process with the Program Activity Tracking for Habitat (PATH) of Fisheries and Oceans Canada (DFO). As long as everything worked, I'd clone that to my main drive or repeat the wipe/reinstall process - whichever is easier for you to perform.Proof of Concept (POC) for mining Program Activity Tracking for Habitat (PATH) A.

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I'd make a junk admin user that I'll delete and use that user to install Xcode and the command line tools and then finally restore from Time Machine. If this happened to me, I'd simply change my Time Machine to exclude system files and then install a new OS onto a spare volume.

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This URL is fairly open (even the search engines can index it) but you might need to make a free Safari or free Mac developer account to log in and get this package.

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Rather than mess with that, why not just download the stand alone installer and wait for a new version of Xcode to clean up your receipts database for you? The command line tools are installed like other OS X packages, so you may need to delete the receipt file from the receipts database (which used to be trivial since you could delete the file from /Library/Receipts but now is more complicated and needs a short article on the receipts database).