• Extract a DeliciousExt data-set with the additional constraint that the chosen del.icio.us tags must also be extractive key-phrases for the documents collected.

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• List all features currently used by machine learning. Feature List
  • ? Include log(1+x) as a feature, in addition to x ?

• Label 100 documents and re-train model (e.g., with Slashdot-type comments)

• Profile freq. of occurrence of features using Weka

• Continue feature engineering to improve classification performance using n-fold cross-validation.

• Work on extraction of keyphrases from multiple documents (e.g., cluster of documents)

• Create a Java interface to support clustering of news items that have been read
  • Save each read news item and document as a “FENA” (Features of Entry and Article) XML file.
  • include meta-data such as time spent, etc.

• Revisit the pre-requisites for going public and gathering data from others.

• Add a button to the RSSOwl interface so that users can give feedback on their interest level in a given news story: like / dislike / neutral . We would like to think about how to incorporate this sort of preference info. into the clustering algorithm.

• Add a suggestion dialog box

• Add results page to design document.

• Investigate automatic feature binarization for Weka to enable use of other classifiers (e.g., maxent)

• Add the category field of a feed entry to the learning algorithm

• Add menu option to ban a Wikipedia category

• Add position features for machine learning.

• Check for stop words before adding a User keyphrase (or is this handled by couldBeKeyphrase()?)

• Sentence breaking

• Try indexing into Wikipedia by single terms, and combinations, and grab all the search results and combine them into something. Prefer all terms first.

• Use Lingpipe named-entity detector as a feature

• Integrate fully into the GUI, recognizing user settings, languages, etc.

• Sync with the newest RSSOwl codebase

• Automatically identify when an article page is just an ad; identify link to true article.

• Ignore comments

• Stop words have to be excluded? I run out of heap space otherwise.

• Go through web page; look at the value part of attribute for either “topic” or “tag”

• Smart folder that is a “meta-feed”

• Context-menu option to add keyphrases from RSS blurb

• Lookup link text from other pages that link to the webpage, via a search engine.

• Revisit Dan's comments on the del.icio.us data and recrawl

• Move your action list here from Word doc.

• Add the feed URL to the FENA

• Add Doc. Freq. to ARFF files

• Create Learner that saves its model to disk.

• Have the Newsreader load the classifier from disk, and use it instead of the baseline model (maybe via switch?).

• Somehow use user ratings to influence the learning model. Maybe just have them submit user ratings for performance analysis, and use the FENA data for more training?

• Experiment using Wikipedia for better keywords.

• Get query search logs from Microsoft Research and ask them about phrase position features in the MoC model.

• Good cut-off? I’m thinking 30%

• Validate user entered keyphrases by checking them for existence in the FENA. What about Wikipedia non-extractive keywords?

• Load the stop words only once

• Cache the last 20 or so new item keyword data

• Grab the Wikipedia article title.

• Rate and save Wikipedia keyphrases

• Thread the GUI somehow? How can we halt it when the user clicks rapidly?