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License: GNU General Public License v3.0
A bookish botscript eager to provide her considerate opinion
License: GNU General Public License v3.0
The parser is pulling out tautological relations like:
[(True, 'yesterday', 'yesterday'), (True, 'today', 'today')]
which are throwing off consistency scoring.
delimit combination/cooccurance detection to within clauses
add chunking in the preprocessing step
script the functionality of: given a word (or article title), grab the wikipedia article on the subject, and strip markup to return text.
For now, don't worry about marking the articles for different handling on the Concept level.
Build demo functionality:
include requirements in a proper requirements file
package for easy installation with pip
Bring in Dave's shallow inference system based on wordnet.
in the preprocessing step, replace pronouns with the chunks
The parser seems to be stripping the first character from some tokens. The knowledge base for "Today was not good. Ice cream is not good. Spinach is bad. I hate everything."
...includes relations like " (False, 'ice', 'ood')" (not missing 'g')
this can go in the bin/ directory at the top level of the package. see:
http://guide.python-distribute.org/creation.html#arranging-your-file-and-directory-structure
Introduce Concept class as wrapper around a semantic node. A Concept has:
Currently, knowledge bases are just built by appending the relations parsed from individual documents in the corpus.
This can lead to the introduction of contradictory relations into the knowledge base.
One way to deal with this would be to check for consistency between Concepts when adding relations to a knowledge base. If the concept in a newly added set of relations is similar enough to an existing concept, then the two concepts can be merged and all relations applicable to either can be adopted. If the concepts are to deviant (despite, say, having the same triggering word), then the new concept can be preserved separately.
After chunking, parse within chunks for relations. Only establish relations between chunks, and internal to chunks.
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