kentonmurray / hw2-team06 Goto Github PK

Use feature coupling generalization, a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources.

1.) Look for an open-source state of the art POS tagger
2.) Integrate with our code using CSE

Use the model as keyterm extractor

Clone the repository to your workspace from GitHub, Migrate UIMA SDK to CSE framework on the project, and push your project back into GitHub as a new branch.

Come up with unique name for files (KeyTermExtractor).

Clone the repository to your workspace from GitHub, Migrate UIMA SDK to CSE framework on the project, and push your project back into GitHub as a new branch.

Come up with unique name for files (KeyTermExtractor).

Figure out ways to improve the passage extractor

This is a Passage Extractor. Not Keyterm Extractor

Investigate how to improve passage extractor

Look at a new scoring function for the passage extractor. Squared Differences?

Try to use this package on our project
https://code.google.com/p/biomedner/

Use feature coupling generalization, a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources.

Clone the repository to your workspace from GitHub, Migrate UIMA SDK to CSE framework on the project, and push your project back into GitHub as a new branch.

Come up with unique name for files (KeyTermExtractor).

Compile these package and use as keyterm extractor

Clone the repository to your workspace from GitHub, Migrate UIMA SDK to CSE framework on the project, and push your project back into GitHub as a new branch.

Come up with unique name for files (KeyTermExtractor).

I have to write my own KeyTerm Extrator

Form as one extractor and updaters structure

The default only looks at passages between keyterms. What about sentences?