The following comprises a collection of my intuitions and “big picture” insights resulting from graduate study focused on text mining at SILS. These are insights related to feature representation, knowledge engineering, model building, the application of statistics to real-life phenomena, and the greater whole of information science.

Many of these apparently go without saying, yet so many discussions of supposed problems would go away if some of these observations were made explicit. This is my attempt to make them explicit. Maybe it goes without saying that expressing the obvious is sometimes quite necessary.

1. Statistical models often fail because they’re missing key attributes necessary to describe the phenomena they represent

Attributes that are altogether unrecognized, difficult to quantify, difficult to analyze, truncated out, or simply forgotten arguably dominate and confound the predictive/explanatory power of statistical models. These missing variable abound. Their absence dominates to the point where theory itself must give way to empiricism and its sister, skepticism. It also means that we simply don’t see everything and that it never hurts to try and see more things.
2. Feature reduction of highly dimensional linguistic data sets is a misguided, outdated and counterproductive approach

There. I said it.

Claude Shannon’s model of information as that which is located among noise is a metaphor that appears to have been misleading a number of people in information science, particularly those involved with anything even remotely tangential to text mining (or, if you must, “knowledge discovery”). Information in an atomic form (e.g., bits) allows for the differentiation of signal and noise. A bit either is a signal or it isn’t. Attributes of real-life phenomena (e.g., average first down yardage in football for a team) are not like bits, at least not in the way we experience them and interpret those phenomena, whether in written explanations or in databases. “Real-life” phenomenae comprise different sorts of real-world features that can never be honestly reduced to their atomic constituents. And, pragmatically speaking, they won’t be reduced to quantum atomic states any time soon.

Given that every attribute of real-world phenomenae we identify partakes of both signal and noise, the removal of any attribute (save for the case of redundancy) always corresponds to the loss of information. Ultimately the statistical modeling of phenomena such as competitive sports and stock markets and clinical emergency room chief complaints is wholly unlike modeling communication channels. There’s something immediately discontinuous about binary electronic signals while other these other phenomena need dramatic interpretive steps before they can be represented with discontinuous electronic signals. Finally, signal and noise are terms that don’t apply very well because that which we are modeling can only be realistically described by features that are both informative and misleading at the same time.

There’s something rather continuous about language (something that latent semantic indexing attempts to capture) and that even the simplest of approaches, such as applying stop word lists to bag of word representations, lost critical information that dictate the semantics of the document. “Dog,” “a dog” and “the dog” quite clearly mean different things, as do “of the dog”, “out of a dog” and so forth. Representing all of those quotations as “dog” or going a step further and representing all of these quotes with the very same word-sense identifier, dumbs down human language beyond recognition. Garbage in, garbage out is a phrase I learned more than a quarter century ago when learning to program games for the Commodore Vic-20.

Reading a text book from 1993 on the C4.5 algorithm, I came across reflections that some crucial elements of C4.5 appeared to be motivated by economizing on computer resource issues. Not enough memory, too slow of processing, etc. In 2007 high performance computing is a commodity. The pressures for feature reduction in machine learning needed to be heeded 14 years ago, but they’re considerably less of an issue today.

Finally, at the very end of my stretch of graduate school studies I accidentally came across a new strategy for feature representation that is so painfully obvious in retrospect it leaves me wondering why no one else has been doing this. Fortunately for Hypothia it spells one very big competitive advantage. But I digress.

3. There’s always something missing from your set of attributes (cf. 1 & 2)

4. There’s no substitute for knowing your data set (cf. 1)I credit this oft-neglected, oft-devalued approach to my first and truly excellent data mining instructor, Miles Efron, who may be to blame for turning me on to text mining in the first place. What have you wrought? He made sure to repeat this lesson of knowing thy data a few times, and the lesson was surely not lost on me. In fact it seems as it it frames and justifies my confidence in my approach.

5. [DELETED] and let your algorithms optimize your attributes for maximal classification margin (cf. 2 & 3)

Can’t say the deleted part yet. But I will, eventually. It probably should be obvious by now. But still I’m not prepared to say.

6. SVM+SMO is very good for binary classification of highly dimensional data (cf. 5)

Improvements to SVM+SMO are always welcome of course, and it appears there are now numerous implementations of SVM that improve. I should note that, according to Eibe Frank, SMO in Weka (written in Java) is just as fast as Joachims’ SVM-light written in C. SMO’s pretty good.

SMO solves the QP problem created by SVM efficiently.

7. You always need more computing power (cf. 2, 5 & 6)

The curse is not dimensionality, the curse is not intellectual. The curse is economic, a problem of resources.
Likely it will be difficult to produce a dataset that is intractable for a good HPC setup running SVM+SMO but it doesn’t exactly hurt to try as long as you’re trying to harness more and more power.

8. You don’t know everything (cf. 3 & 4)

9. models only forecast well in forecast-influenced environments only when the model has an information advantage over other models (information assymetry, competitive advantage)

10. You’ll never get it quite right ( cf. 8 )

11. There always more left to do (cf. 5, 7 & 10)

12. Disambiguation can be better pursued not in any pure sense by machinic strategies but rather by messier approach of utilizing the greater context surrounding term, document, and corpus, which in turns permits some degree of ambiguity, which is necessary for understanding

13. Word sense disambiguation is quite possibly the wrong way to go to conjure semantics in one’s text representation (cf. 2 & 12)

As I’ve written before, there are other approaches available to leverage semantic information that are better than word-sense diambiguation (WSD) .

14. More formally, the incorporation of ambiguity into linguistic representations (i.e, representing all possible word senses/meanings and POSs for any given word) allows for better representations of intelligence than ones produced at least in part through WSD strategies

15. For artificial intelligence to become smarter than humans, it must at least be as smart as humans first.  A person’s ability to understand multiple senses of a given word at once (of which poetry is perhaps the most striking example) is strikingly intelligent and far more intelligent than most WSD approaches I’ve seen (cf. 14).  And when you consider that the basic unit of meaning is truly not the word but the sentence, WSD seems all the more foolish, and yet makes me feel there’s a huge opportunity to understand language from its wholes and holes.  Discourse analysis anyone?

16. Not knowing everything, not always getting it right, and always having more left to do makes the hard work a great deal of fun. Discoveries are everywhere waiting to be written into existence. (cf. 8, 10, 11)

17. Don’t panic, be good, and have fun. (cf. 16)

18. The essence of human language is nothing less than the totality of the human language in all of its past present and future configurations and possibilities.

Hypothia is the name of my new venture. Hypothia is a new organization dedicated to innovation leveraging the power of text mining and advanced analytical strategies for vertical domains. Hypothia aims to release a set of next-generation information tools with the ultimate goal of replacing search with generation.

The shift from information retrieval (IR) to information generation is a subtle yet revolutionary shift in the way we interact with information.

On this blog I hope to discuss various strategies, technologies and general ideas that might contribute to this paradigm shift. As Google moves away from a business strategy of innovation in IR to a strategy of product/service diversification, they create a tremendous opportunity for everyone else to invent the next best solutions.

I have a number of core interests with respect to text mining. I believe in the concept of know thy data. Hence I believe that myriad complexities in text mining can be reduced and application usability can be maximized by concentrating in specific problem areas. Most of my own work has concentrated in health, from drug discovery to consumer health to clinical diagnosis. I also have a fascination with applying mining strategies to other areas, such as content management, commodities forecasting, real estate pricing analysis, and even sports analysis.

If you wish to learn a little more about me, please see my personal page (http://patrickherron.com). You can find additional information about my previous academic research on my text mining co-op search page (http://proximate.org/tm) and you can learn more about my creative writing and publications on my writing bio page (http://proximate.org/bio).