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The Natural Language Toolkit (developerWorks)
IBM developerWorks covers
a Python library for applying academic linguistic techniques to collections
of textual data. "For this first article, I will present some
relatively fleshed out examples from the lower-level capabilities, but
simply describe abstractly most of the higher level capabilities. If I have
the opportunity to return to NLTK in a later installment, I will give more
detailed descriptions of parsing and graphing; for now, let us take the
first steps past text processing, narrowly construed."
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The Natural Language Toolkit (developerWorks) Posted Jun 25, 2004 20:08 UTC (Fri) by iabervon (subscriber, #722) [Link] One thing I've been thinking someone should write for ages now is a mail client plug-in to list mail messages by top 5 porter-stemmed words by term-frequency-inverse-document-frequency. When I was playing with automatic topic extraction, I found that this feature was quite distinctive, and often more useful than the subject provided by the sender.Now I miss reading words like "whatev" and "relat". Also, the singular of "corpora" is "corpus", not "corporum".
The Natural Language Toolkit (developerWorks) Posted Jun 25, 2004 22:15 UTC (Fri) by cpeterso (guest, #305) [Link] Pardon my ignorance <:) but what does "top 5 porter-stemmed words by term-frequency-inverse-document-frequency" mean? Does this mean the most common words in a particular email that are least common among ALL emails? i.e. what words make this email special compared to all other emails?wow, that is a very interesting idea you had! :D
The Natural Language Toolkit (developerWorks) Posted Jun 25, 2004 22:58 UTC (Fri) by iabervon (subscriber, #722) [Link] Right. You first look at all of your emails (so far), and count how manyof them each word appears in. Then you look at the new email, and count how many times each word appears. You divide the latter by the former, and then show the words which score highest. Note that you stem the words beforehand, so "frequent" and "frequency" are counted together. Also note that, when looking at your whole corpus, you count the doucments that a word appears in, not the total number of times the word appears. So you wind up with the words which are used frequently that are in a small portion of your email. It's not actually my idea. I encountered it when using the Remembrance Agent's engine, which got it out of the document comparison literature from around '96 (as well as how to optimize computation of these vectors for a corpus and how to compare vectors efficiently), but as far I know, nobody's done software where you actually see the top words, as opposed to having the software use the information internally (and I don't know if anyone's noted in the literature that the top five words are often helpful; people generally use all of the words). It's the sort of thing where the debugging code turns out to be really interesting. Our engine for clustering documents was showing its results and the info about individual documents; I was using emails, and realized that I could tell when the emails were about from just the display.
The Natural Language Toolkit (developerWorks) Posted Jun 25, 2004 23:22 UTC (Fri) by dang (guest, #310) [Link] You might be able to do something like this pretty easily with OpenFTS.
The Natural Language Toolkit (developerWorks) Posted Jun 25, 2004 23:41 UTC (Fri) by cpeterso (guest, #305) [Link] Thanks for the explanation! Do you remember an example of any particular email's top 5 words? This is a cool idea, but it sounds like a lot of work (programming and compute-time) to create email summaries. I bet most people don't spend much time reading or relying on their emails' subject lines. :\
The Natural Language Toolkit (developerWorks) Posted Jun 26, 2004 0:29 UTC (Sat) by iabervon (subscriber, #722) [Link] I don't remember a good example, but the most of the programming work isalready done in the remembrance agent, available (GPL) from http://www.remem.org/, which indexes a corpus and then can efficiently query it. The interface only really supports finding the documents which match best, but the engine has the words and their scores internally. It's not a lot of computation provided you do an initial scan and then update it incrementally; using the stored info is too fast to tell on my machine. (Note that what it displays for a match are what matched, not the top words for the document in general) Querying for one of the emails in the list gives, for it's match with itself: "window, throwing, knife, pizza, stuck". I'm not sure whether this is only completely clear to me due to remembering the email, or whether other people could also figure out what the email was about from that information.
The Natural Language Toolkit (developerWorks) Posted Jun 26, 2004 17:44 UTC (Sat) by jwb (guest, #15467) [Link] You might be surprised how effective this technique is. By seeding with a single email or email thread, you can fairly effectively find clusters of similar emails in a mailbox. It isn't perfect, but it works, and if you allow iterative negative or positive user feedback, it converges on the desired behavior within one or two rounds.I use rainbow for such work, and unfortunately it can take several hours to stem and lex my maildir :( Maybe in some ideal future someone will come out with an improved libbow.
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