May I present: kompressr

[alexr_rwx]

kompressr: make text shorter harnessing the power of acronyms (MTSHTPOA)

Try it out, let me know what you think and if it breaks :) Should be useful for making long papers shorter, by automatically extracting acronyms and using them wherever possible.

Public beta!^tm

(running on App Engine, with NLTK!)

Comments

[lyceum-arabica.livejournal.com]

(laughs) Very cool! ...with some pretty funny pre-tweaking results. I fed it the first couple paragraphs of the wikipedia article on space flight, and it decided that 'Space Exploration' (SE), 'during the' (DT), and 'of Space Exploration' (OSE) were all good picks :-) This isn't my area, but I'd think the problem of identifying important common phrases particular to a piece of work (like SE), while weeding out generally common phrases and things that are a concatenation of the two, might be paper-worthy.

Also, you might want to keep it from acronyming its acronyms:

"Various criticisms of SE (OSE) are sometimes made. SE has often been used as a proxy competition for geopolitical rivalries such as the (AT) (SAT) Cold War. The early era OSE was driven by a "Space Race" between the Soviet Union and the United States (US); the launch of the (OT) first (TF) (OTF) (TLOTF) man-made object to orbit the Earth..."

[lindseykuper]

I wasn't a fan of the acronyming-of-acronyms, either, but Alex claims that that's a feature!

[oniugnip.livejournal.com]

I tried it both ways :)

[oniugnip.livejournal.com]

Thanks for playing with it! :)

This isn't my area, but I'd think the problem of identifying important common phrases particular to a piece of work (like SE), while weeding out generally common phrases and things that are a concatenation of the two, might be paper-worthy.

It's a good intuition that that's an important problem, but it's way been done. There's a whole literature on finding sequences that commonly appear together (say, in a set of documents, or characteristically to particular documents)... words that NLP/corpus linguistics people say when they're discussing such a thing include "collocation (http://en.wikipedia.org/wiki/Collocation)" and "TF/IDF (http://en.wikipedia.org/wiki/Tf-idf")", if you're interested.