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FRDCSA | internal codebases | Classify

Classify Architecture Diagram: GIF

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Project Description

Capabilities

• Develop a decision tree classifier so that I can ask straightforward questions to classify functionality.
• I should create a system capable of running its own experiments and organizing its experiments for the purpose of training a classifier, and from this, be able to perform new tasks. For instance, to classify the above entries.
• Write code to classify the entries in my todo.kif file into projects.
• Should have a bard -d record,index,classify
• Use question classifier (QC.tar) to classify searches as well
• Should read my early writings, but classify out angry parts.
• Could classify corpus entries along emotional lines.
• I mean classify
• This will be useful in creating the models. Note, we should also classify interest in topics, not just documents, since document granularity is not as meaningful. Also, I suppose we can tag authors as being good or not.
• We will use a classifier to classify writings into topics automatically using the code I just wrote for critic.
• classify emacs logs as legitimate versus illegitimate, clean them and use text classifier.
• The functionality classifier help to classify items that are marked generally as requirements.
• corpus can determine when there is not enough information available for a given classifier to classify an Item, in which case it does various checks, defaulting to asking the user.
• Need to use corpus to classify email/aim logs for what to do with them.
• using kissinger corpus, formalize domains they are discussing, and represent communication actions formally, classify them, etc.
• Use text classification tools to classify resources into appropriate categories.
• Use Tabari in audience to classify communications.
• There are various methods of doing matching between agents and capabilities. Perhaps subsumption reasoning is overkill. For instance, maybe we can just use various texts that the user has written and use bayesian text classification to classify problem descriptions to these texts to determine who is likely to be interested in them.
• If it's not obvious, unilang will be using corpus to classify the users entries.
• numerous ideas on the subject: should have classes be a type hierarchy. Maybe build a grpahical tool to interactivcely classify these. Convert these system over to kbfs::Cache, keeping a copy of current method. For each icodebase and common (agentified), add a class. For instance, rather than classifying into a vague category, you could classy directly to "pse, goal", etc.
• RSR can use the critic::Classifier system to classify its events into habits.
• corpus must first chunk, then classify.
• We should have it so we have to review and classify all thoughts at the end of the day.
• audience should use ConceptNet's ability to judge the gyst and the emotion of a letter to classify angry letters. We should see how that works after setting up the XMLRPC server.
• You can use typing speed to (help) classify related thoughts in corpus.
• You could use a classifier to classify text files into various soft clusters for projects.
• Also, note that, presently Memcons are difficult as classify is not complete.