Section 1 outlines the problem of training, namely obtaining a good
sample of text to be labeled for the trainer.  After disposing of
several other methods of garnering samples (random, relevance
feedback based), Lewis and Gale introduce an iterative approach for
manually labeling examples.

Section 2 then discusses the benefits of “learning by query” in
theory, namely the possibility of reducing the error rate very
quickly in comparison to the number of queries required.

Figure 1 (described in section 3) outlines their basic approach,
which relies on having a human judge some subset of examples that the
currently used classifier is least certain about.  This process is
iterated until the human feels satisfied with the results.  One
caveat of this approach is that the classifier must not only predict
the class, it must give a measurement of certainty for that class.

Continuing on into section 4, we are introduced to how to build a
classifier and use uncertainty sampling to train it.  Most of the
section details the probability theory behind it, finishing up with
how to do the sampling.  One thing I always wish for in these papers
are concrete examples (maybe as an appendix or a reference) that work
through the math on an actual toy problem.  Section 5 does just this,
laying out an experiment and discussing the details, minus the math,
which probably suits most people just fine.

Section 7 has an excellent discussion of the results, the pay dirt
being that using this new method significantly reduces the number of
examples required for training, at the cost of having a human in the
loop.

Text classification is the process of assigning a document to one or more categories (we looked at classification/categorization earlier when exploring Support Vector Machines, SVMs).  My understanding of the difference between categorization and classification is that categorization has a set number of categories, whereas classification  does not.  At any rate, this paper is  comparing two different classifiers that  use  a naive Bayes approach.  The naive Bayes approach assumes that all attributes of the examples we are studying are independent of each other.  Even though this is rarely true in the real world for text (after all, if I chose all the words of this post independently we probably would have gibberish) it turns out that it still works pretty well in practice.  But I digress… The two approaches that are being compared are the Bernoulli model and the multinomial model.  The Bernoulli model uses the document as an event and builds a vector of binary attributes based on whether a term occurs or not in the document.  It DOES NOT take into account the number of times the word occurs.  In the multinomial approach, words are the event and term frequency does matter.  The next couple of sections after the introduction layout the common ground between the two approaches as well as the differences.  The differences come down to how the probabilities are calculated.

There is some interesting discussion of feature selection (a way of reducing the size of the vocabulary, which speeds things up, without, hopefully, losing too much information) using mutual information that is worth digging into a bit more if you have the time.

The next sections are where the rubber meets the road and the authors do a side by side comparison of the two approaches using 5 different collections.  You can see the results on pages 5 and 6.  Finally the discussion of the results occurs on page 6 and 7, with the bottom line seeming to be that the multinomial model seems to “be almost uniformly better than the multi-variate Bernoulli model.”

For those interested, Weka has tools for building Naive Bayes classifiers.

After covering some related work about Latent Semantic Indexing (I suppose I should look into that some day), the authors delve into the methodology of identifying phrases and their histories. There are 3 steps to the process: 1) identify frequent phrases, 2) generating histories for the phrases and 3) identifying the phrases for a given trend.

Phrases in this paper go beyond the simple sequence of terms, introducing the notion of a “k-phrase”.   A k-phrase is essentially a nesting of phrases and they can span sentences, etc. when appropriate.  For the histories, each word gets a transaction id and associated timestamps.  Then, given these bits of informations, the authors use a shape query language to mine the phrases and histories.  The shape query language allows the user to specify they are interested when items are “spiking” or “trending downward”, etc.  There is a reference for the shape language in the paper.

Finally, the paper ends with a discussion of how IBM used the approach in a patent mining system to  identify trends in patents from the US Patent office.

Next week, I think I am going to start looking into things like event detection, etc.  However, I am also considering looking into some non-NLP areas related to data mining, so if you have a preference, let me know.  I am open to explore many new ideas in the field.