Empirical Measure of Reliability: Part Four

This fourth installment on Twitter predictions for the NBA Finals will wrap up, for now, my pilot study on measuring reliability as a function of qualities that are detectable in the writings of the individuals who make the predictions. I've made some observations in the three preceding posts, and here I will make a few more and summing up the overall results.

First, the lay of the land: I collected predictions that individuals made on Twitter regarding the 2011 NBA Finals. The predictions were of which team would win and how many games the best-of-seven series would last. This gave eight possible outcomes, which ranged from one team winning in a four-game sweep to the other winning in a four-game sweep. Besides the individuals making predictions, there were other ways to quantify the soundness of each possible outcome, and the odds posted by Las Vegas sports books offer a possible "rationalist" position. At least, if there is a systematic error in the sports books, then there is a way for someone who knows more to get rich.

The sports books predicted a fairly close series, with Miami favored, but only slightly, with Miami-in-seven as the most likely outcome. As such, the least-favored outcomes were blowouts -- Miami-in-four or Dallas-in-four. Even before the Finals began, those seemed to be the brashest/wrongest predictions. And that proved to be true very soon, as each team won one of the first two games, making both of those predictions already wrong. Note that in some other year, where one team was dramatically superior to the other, predicting a four-game series might have been the smart bet -- but that was not true this year.

The heart of this study is to analyze the text that these 165 Twitter users have offered in other tweets (not the specific single prediction itself) and see if there is a fingerprint for reliability in the text that people generate.

Here are the observations that I have previously noted:

1) The people who made the rashest and most-incorrect predictions, a four-game series, differed from the crowd in being less prone to punctuate their tweets. (Note: There were only six such individuals, so this finding may not be significant.) They did not stand out in other obvious ways, such as the use of capitalization.

2) The people who made more extreme predictions (four or five-game series; including the "5s" raised the sample size to 32) were more likely to use the modal verb "must" than the modal verb "might." This is suggestive that some people carry a black-and-white worldview around versus those who see things in shades of gray; the "must" people predicted a lopsided outcome despite the judgment of Las Vegas (and the actual outcome of the series) that was less extreme.

3) While the frequency of the eight possible predictions roughly matched the Las Vegas probabilities (Pearson correlation r=0.56), the crowd deviated from this primarily in over-predicting Dallas-in-six. Interestingly, this ended up being the actual outcome of the series! Is there a significant minority out there (about 25% of the individuals) who know better than Vegas?!

Now, two observations that I have not reported previously:

4) Do those Dallas-in-six predictors show some sign of brilliance in their vocabulary? The words most apt for those people to use more often than the other 75% of individuals were: {awesome, follow, through, morning, maybe}. The words they used less often than the others were: {watching, fun, free}. If there's anything plausible about a worldview here (as with the "must" vs. "might" observation) I don't see it.

5) We can rate the full set of predictions according to correctness (Dallas-in-six being exactly right, but every other prediction being a certain number of games away from this, ranging from one game off to five games off). And then we can correlate correctness with the gross properties of the individuals' tweets and we see that correctness correlated positively with:

A) People who type longer tweets were more accurate than those who type shorter tweets. Pearson correlation, r=0.48.

B) People who use more mixed capitalization (capitalizing the first letters of words, vs. leaving the whole word lowercase or all-uppercase) were more accurate: Pearson correlation, r=0.46.

C) An overall measure of "fluency", using the correct English function words like "and" and "be" correlated only slightly with correctness: r=0.08.

I stress again that this study was not painstakingly scientific, but I would like to use it as a pathfinder towards more informative studies in the coming months. An ambitious-enough goal would be to assess which individuals write in such a way as to seem to be systematically deluded, showing the world that they dispense with facts and wisdom and draw their own conclusions anyway. A yet-more ambitious goal would be to distinguish those individuals of exceptional reliability from those of average reliability, and perhaps to use the crowd as a predictor of the future that is better than anyone has yet systematically recognized (imagine if this led to predictions that were smarter than Las Vegas tends to posit).

While it will be fun and informative to continue this work with other sports events (a ready source of quantitative predictions that can be graded objectively), it would be even more rewarding to evaluate the soundness of predictions regarding politics, policy, technology, and science. It would of course be useful to analyze qualities that are deeper and more meaningful than punctuation. And I will confess to an ultimate goal of collecting empirical statistics on the soundness of various kinds of higher reasoning. Can we do the same as I've done here with arguments that intelligent astronomers made in the Twenties through Fifties, grading those predictions with the correct answers that we now in many cases have? Can we have a sort of truth-o-meter based on this sort of empirical work? Or, at least, can we show people that if they write badly they will seem less reliable? Watch this space in the months to come.

Empirical Measure of Reliability: Part Three

The NBA Finals ended last weekend with the Dallas Mavericks beating the Miami Heat in six games. This is an outcome that a significant minority of the Twitter users predicted -- specifically, it was the second-most common prediction (out of eight logical possibilities) with 24.8% of users choosing it.

What's interesting is to see how the predictions of the crowd selectively followed the probabilities implied by the odds posted by Las Vegas sports books. In the graph (click t0 enlarge), we see the possible outcomes along the bottom, with those most favorable to Miami on the right and those most favorable to Dallas on the left. The house (red line) gave Miami a modest edge in the series, and the probabilities hint at a Gaussian with a peak centered on "Miami winning in seven games." If we ignore the "Dallas in six" position, it looks like the crowd largely went with the gambling odds, choosing a peak that was nearby (Miami in six instead of seven) and then following a course somewhere between Response Matching and a Winner Takes All preference for the favored outcome.

However, the crowd deviated from that in one big way by giving far more credence to the "Dallas in six" outcome (and slightly more to "Miami in six" and slightly less to "Miami in seven"). It is specious to read too much into this single instance, but it looks like the crowd -- a significant minority of them -- got smart in a way that Las Vegas underestimated. Do those Dallas-in-six people have some special talent, or did they just get lucky? I'll take a look next time at how the Dallas-in-six predictors differed -- or didn't -- from the other predictors. Is there a smart gene somewhere in their writing?

Empirical Measure of Reliability: Part Two

My pilot study on the use of text analytics to determine reliability is underway. The idea was to log a lot of predictions regarding the NBA Finals before the event begins, then to analyze the text generated by the predictors and look for correlations between a person's language and their accuracy in predicting the future event.

This is not a proper experiment -- there's a lot of hackery on my part. My hope is to learn from this pilot study to gear up for a proper experiment in the coming months. As of this writing, two games in the best-of-seven series have been completed, with the Miami Heat and the Dallas Mavericks each having one win. At this point, I'll give a quick overview of things I've seen in the data, which I collected right up to the last hour before the series began.

First, the predictions themselves: I collected 245 predictions from users on Twitter. I chose only predictions in which the user specified the outcome in terms of the series winner and the total number of games. (A best-of-seven series ends whenever one team has four wins. This could happen in as few as four or as many as seven games.) The object of interest to me is to look at the text these users produce in posts besides the prediction post itself, so I cut the study to those 165 posters for whom I was able to collect at least 10 other Twitter posts (excluding those which are retweets, containing the posts of other users).

The users tended (59%) to prefer Miami and the single most common prediction was Miami to win in six games. This is not far from the predicted outcome implied by the Las Vegas odds, which favored Miami in seven games, with Miami in six as a close second. However, the overall profile of the Twitter predictors in some ways deviated quite a bit from the Las Vegas view.

The Twitter predictors have a strange overestimate of the likelihood of Dallas (the underdog) winning the series in six games. This is strange in that the odds predict that the most likely duration of the series is seven games (34% probability). However, 59% of Twitter users predict a six-game series (most favoring Miami, some Dallas). Why? Maybe the answer lies in history: Six games is historically the most likely duration of an NBA Finals (41.5% of the Finals since 1980). Maybe people begin by accepting the likely duration of the series, and impose upon that their selection of which team will be the beneficiary. If so, there is an illogical bias: If Dallas is to perform better than the odds predict, then it is more likely that Dallas will lose in seven games, or win in seven games. It seems that users who favor Dallas, however, "go big" in their favoritism, concluding that if Dallas is to do well, they will do really well, winning by the comparatively large margin of four games to two which the odds say is fairly unlikely (10.2% probability).

Looking at the gross breakdown of predictions, it is also interesting to consider those who predicted the series to end in just four games. At a glance, it looks like the predictors are conservative, with only 4% having predicted a four-game series while the odds give a 13% probability of that outcome and history telling us that 17% of Finals end in four games. However, seen another way, the predictors seem irrationally exuberant in predicting a four-game outcome. Given an objective determination of the probabilities, it is irrational for anyone to choose the least-likely outcome. Let's say that we had a six-sided die with one side marked A, two sides marked B, and three sides marked C. If we ask a million rational gamblers to bet on the outcome of a single roll, it's not that 1/6 should say "A"; rather, absolutely nobody should say "A" if they want to maximize their probability of being correct. That 4% of predictors predicted a four-game series sweep (four choosing Miami, two Dallas) indicates either that those predictors (and to a lesser extent, those choosing a five-game series or Dallas winning in six) are responding irrationally. Maybe for one of these reasons:

1) They incorrectly believe they know something that the rest of the world doesn't.

2) They actually do know something that the rest of the world doesn't.

3) The payoff for these predictors may favor winning and losing unequally. They may get great prestige or psychological reward from making a rare, perfect prediction, whereas they can simply ignore or walk away from their incorrect prediction should it not come true.

Interpretation (2), I should add, is not very persuasive. If a class of individuals had the ability to predict more accurately than the rest of the world, then some of those individuals would have the incentive to bet large sums of money on the event, which would shift the odds to the correct value. The odds should reflect the "smart money" quite closely unless someone has access to information that the world does not. (E.g., if the series is "fixed" by a point-shaving scheme.) It is unlikely, to say the least, that people posting on Twitter have rigged the series or are in on such a scheme and have decided to act on that information by posting a prediction to Twitter.

It is premature to call the study complete, but I will quickly note some characteristics of the data. First, I noted how much punctuation (particularly, periods, commas, or apostrophes/single quotes) per tweet each user produces. The average over all predictors is 2.34 punctuation marks (of those kinds) per tweet, with 60% of predictors using more than 2.0 punctuation marks per tweet. An interesting, if not significant observation: Of those users who predicted a series duration of five or more games, only 38% used fewer than 2.0 punctuation marks per tweet. Of those (only six) users who predicted a four-game series, 100% used fewer than 2.0 punctuation marks per tweet! Are the people who ignored their English teachers walking through life ignoring all sorts of wisdom, making basketball predictions as badly as they punctuate? There's no statistical significance in this result, but note that the four-game predictors are already wrong: Because each team has already won a game, the series will last at least five games.

Performing a similar analysis of how people capitalize has not shown any such effect, however. If people who punctuate poorly are ignoring reality, the same is not obviously happening with Twitter capitalization.

It is also going to be interesting to note the vocabulary biases in the various groups of predictors. Those who used the word "must" in their non-prediction posts had a 32% probability of predicting an extreme, and unlikely, outcome (four games or five). Only 12% of users who used the word "might" in non-prediction posts predicted such an outcome. Does this mean that there are people who see the world in black-and-white, who ignore the more likely median cases, the shades of gray? Perhaps! In future studies, it will be useful to collect more data to look at a wider range of terms that indicate a black-and-white worldview or a shades-of-gray outlook.

That's the mid-course report. I will provide more analysis as the results come in. And the tangible (if not statistically significant) determination of how accurate the predictions were will come courtesy of the Miami Heat and the Dallas Mavericks. Tonight: Game Three!

Empirical Measure of Reliability: Part One

Students writing essays. Contributors to Wikipedia. Economists, Wall Street traders, technologists, scientists, and political pundits predicting the future. A general enterprise for thinking persons is to understand the world, sometimes vying against the adversity of uncertainty. In various ways, those thinkers who venture an opinion are "graded" for the quality of their work, and those who do well are -- perhaps -- accorded more credibility in the future.

Separately, text analytics is a technology that is coming of age. Low-level properties of text are used to assess the meaning in interesting ways. I have worked to build business-to-business solutions in text analytics -- netnography at Netbase and I am currently building sentiment analysis classifiers in many languages for Meltwater News.

When one reads analysis (as when I graded research papers as a teacher at Western Reserve Academy), one inevitably feels that some writers show a high degree of insight while others analyze poorly and show this with writing of lower quality. If an analyst cannot construct a proper sentence, then doesn't that say something damning about the quality of the analysis? Is a tongue-tied politician inevitably a hack in determining policy? Can someone who confuses "there" and "their" have something worth saying?

Here, I announce a pilot study to examine if the low-level properties of text have a bearing on the quality of the analysis therein -- more specifically, the accuracy of predictions made by the analyst. Choosing an objective proposition that is close at hand, I will use the 2011 NBA Championship Series, which begins a few hours from now, featuring the Dallas Mavericks vs. the Miami Heat, as a test of the many people out there who are staking their reputation upon predictions of the outcome. In the era of social media, such predictions are not scarce. My experiment is to collect the identities of many Twitter users who are predicting the outcome of the series. Then we can analyze the low-level qualities of the text that these users produce and have an objective measure (albeit with very low N -- just one yes/no "grade") of the reliability of each user's predictions.

The provocative promise of this type of study is that we can find empirical evidence that people who express themselves in certain types of ways are better predictors than people who express themselves in other ways. Imagine the range of possible discoveries. Imagine the gleeful English teacher who can point to empirical fact to conclude once and for all, "People who do not capitalize and punctuate correctly think poorly and what they say is factually incorrect." Imagine the shock in academia if the reverse proves to be true!

To be clear, this pilot is far from a proper experiment. The "low N" problem (only one result) means that there cannot be much meaning in the result. Maybe the smart prediction will turn out to be wrong (say, if a player on the team that should have won suffers an unexpected injury). I am not carefully picking my variables in advance -- I will analyze them as the series takes place. And there is plenty of subjectivity even in the determination of what prediction is being made (the language of these tweets is very vague). Also, the independent variables will necessarily be low-level (like use of punctuation) and preclude the more interesting high-level variables like reasonableness-of-argument. I think a proper scientific analysis could find so many flaws in my methodology to disgrace me thoroughly.

But the topic, is for me, compelling, and can serve as a pilot for better studies in the future, addressing many of those shortcomings in future work. Call this observation instead of science.

I have been collecting prediction posts from Twitter over the last few days, and I continue to do so at the present moment. It seems that I will have roughly 200 users in my sample, with about 50 posts per user as the text that can be analyzed as a sample of their writing. I will separate the users into groups after the series begins this afternoon, and then we can see how the best-of-seven series speaks to the quality of the predictions.

I hope that this the beginning of future studies, extending the scope to other domains (e.g., election outcomes, public policy, the success of Internet startups), and more subtle qualities of the analysis (e.g., use of causality to explain one's points; proper use of logical reasoning). And maybe one day, we will be able to take much of what might be written and say, "That kind of thought is invalid." And maybe the world will raise the quality of its thought by a notch or two. Isn't it be pretty to think so?

My data collection continues. Predictors are typing away. And over the coming days, the Miami Heat and the Dallas Mavericks will help us determine what is right and what is not right.

Medicine for healthBase

Public Showcase Gone Wrong

Last week, TechCrunch reviewed healthBase, a public showcase of the Natural Language technology coming out of NetBase Solutions. In a rapidly-developing turn of events, TC published Leena Rao's brief and largely glowing review. Then the comments came and absolutely destroyed it, with phrases like "total fail" prompted by search results that were alternately terrible, hilarious, and if some posts were taken at face value, offensive. Hours later, Rao posted a second review picking up on the criticism.

Most of the criticism is to some extent fair in that healthBase does readily yield lots of bad results. However, some of the critics go on to hypothesize how the system works and make incorrect conclusions. Worse yet, some bloggers have used this as an indictment of the state of Natural Language Processing in general.

I'm in an unusually good situation to comment, since I was a founding engineer of this technology, building the original Natural Language system at NetBase back when it was called Accelovation. I'm disheartened to see the public rollout of the technology turn out like this, particularly in that some of the fixes to the evident problems were on the agenda when I left the company two years ago. There really is a strong technology at the heart of this system, and with a couple of fixes, this rollout could have been much stronger. I don't know why the low-hanging fruit that could have fixed these problems wasn't plucked in the last two years, but the solutions are clearly identifiable, so let me describe here the two specific technological fixes that are needed, plus one other crucial bit of wisdom.

Treatments for Bad Results

TechCrunch's second review made much of a bad set of results for "Causes of aids" (meaning, of course, Acquired Immune Deficiency Syndrome, not the verb to aid). In the initial set of results (the company has worked rapidly to clean up the kinds of results that drew all the criticism), the top two results were good, although loosely referring to the same thing: sexual contact with an infected partner. The third result, virus, was also quite valid. But the next seven were all downright bad. To an outsider, they ranged from the bizarre (strong magnetic field) to the equally bizarre and arguably offensive (Jew). But as an insider, I can tell you that there were two causes of bad results, and the system can get a lot better if these are fixed.

Tell Me Something I Don't Already Know

One of the results for the "Causes of aids" search was the singularly unilluminating Feature. When you perform searches on healthBase now, feature never comes up, indicating to me that they placed it on a blocked list of possible results. This was an initiative that we knew was necessary back in 2007, and was something I was working on at the time. Somehow, this work stopped far short of the goal after I left. Adding feature in 2009 is not the necessary general solution, because scads of similar terms are still coming up tonight: A cause of measles is characteristic. A cause of blindness is disorder. A cause of malaria is objective. A cause of leukemia is defect. Terms like feature and characteristic are too general to make sense in any circumstances. Terms like disorder and defect carry just one bit of information: They are something unfortunate, and of course, you could say that most bad things like AIDS are caused by some defect or other. Most things, good or bad, can be said to be caused by a characteristic -- the thing someone would want to know is -- which characteristic of what? In the case of measles, the extracted sentence tells us that it's a characteristic of "immune priming" -- something the system should have and could have extracted instead of characteristic.

The simple logic to fix these problems is to have a list of such terms and never show them. That's not a project for an all-nighter or even a couple of months' work, but in two years, they should have come up with an exhaustive list -- the top such terms identify themselves pretty easily by being pervasive; they're vacuous because they're omnipresent -- but didn't. These vacuous terms are the less-numerous and less-glaring of the bugs that have lit up the blogosphere, but they stem from a clearly identifiable source of bad result that is easily fixed.

Safety in Numbers

The worst kind of errors are results that defy common sense -- statements that Jew and strong magnetic field are causes of AIDS, or that Rancho del Arroyo mares cause hookers. NetBase's very talented Jens Tellefsen correctly identified -- in part -- the root cause of one of these errors, a single sentence on Wikipedia (and echoed elsewhere on the web) that juxtaposed "Jews" with "aiding". To wit:

Hispano-Visigothic king Egica accuses the Jews of aiding the Muslims, and sentences all Jews to slavery.

Clearly, there was an error in the parsing. The obvious (to us) use of AIDS as a noun was confounded with the system parsing the verb aiding to its stem aid, and somewhere along the lines, not seeing the difference between the two. Accusation indicates that the thing described is bad, so the parser concluded that Jews did something bad involving aid. We could go into greater detail, but the gist is, that noun-verb error was made between the parsing of that sentence and the interpretation of the pithy search term AIDS. Jens called the problem out, and drew an unfair backlash, including:

Personally, I think such basic distinctions should have been ironed out before launching the site.

and

I am sorry, but if you are purporting to be an intelligent search engine, you need to be show basic intelligence like being able to disambiguate from different meanings and tenses of words. You need to be able to identify parts of speech, especially if you are trying to find references to causes.

and

I hate to be pedestrian, but isn't that just a fancy way of saying it doesn't work?

All three of those rebuttals are misplaced: It's a simple fact that no Natural Language system will get the meanings and tenses (and, most to the point here: part of speech) correct 100% of the time. You don't iron out those distinctions, get a perfect parser, and then launch your product. Jens is quite right in asking the critics to excuse the occasional misparse.

However, this result is obviously a failure in the rubber-meets-the-road sense, and the critics miss the real problem: It's not that the parser could make such a mistake: It's that such a mistake was allowed to produce a result which was ranked fourth! And there's an easy fix. Whatever formula you use to rank results (sheer number of occurrences being a likely but mistaken candidate) the system should only allow a single specific sentence to count once no many how many times it is repeated across the web. Once you accept that principle, don't display any results based on so few occurrences that the inevitable imperfections in parsing could allow such a result. And then -- problem solved!

The "Causes of aids" results claimed that there were 116 records retrieved, and the top twenty causes were displayed. If "top" had been expressed in terms of number of specific sentences that provided that particular result, then Jew would have gotten a score of 1. If a threshold of, say, 2, were applied as a minimum number of evidentiary sentences required for a result to be displayed, the single most lampooned result of this launch would have been avoided -- along with most of the other bad results. In several cases where I currently find unintentionally humorous results, a Google search reveals the single sentence that caused the problem. Now you'd only run into trouble if multiple writers had produced the same anomalous result with alternative phrasing -- set your threshold according to the precision of your parser and the size of the database, and you can make the probability of such errors as low as you'd like -- a very desirable choice of precision over recall.

Again, a simple fix, and one that I'd mentioned back in 2007, but that was never put into production at the time. But it's totally crucial to filtering automated Information Retrieval results and achieving reasonable quality. If a system allows results based on one misparsed sentence to go public, it is going to show crazy results.

Pick Your Battles

When I found out that the domain of health and medicine was being used as a showcase for Netbase technology, I was very surprised. Because back in 2005 and 2007, when we were seeking out venture capital, a key point was that the technology was meant to be generic and not about "vertical", single-domain searches. When Medstory launched, we had some internal discussion about it, and I made clear the point that medicine is an area where an ontology is almost sufficient for structuring searches in the way that Accelovation (as it was still called) used NLP to do from scratch.

To be more specific, the entities in medicine are usually confined to one semantic category from the following list: "Drugs and Substances", "Conditions", "Procedures", "People", etc. -- the main result "silos" for Medstory results. As a result, they don't need to understand the meanings of sentences -- if a drug is mentioned in a result on the search topic, it is automatically placed in the "Drugs and Substances" column with an almost 100% chance that it is not being misclassified (have you ever met a person named "Thiamine"?). As a result, information retrieval in medicine can be done quite well without semantic search, and in fact, semantic search is more likely to surface errors unless the results are filtered through the two face-saving mechanisms I mentioned above. If you wanted to trip up the ontology-based approach, you could do it by being clever: Back in 2007, I typed "vitamin B poisoning" into Medstory and it listed "Vitamin B" as a drug -- be careful with that advice. But the exceptions are few, so Medstory already had the problem solved better than healthBase really had a chance to do in 2009. (And the problem I mentioned has since been fixed, showing that Medstory's team has not been complacent.)

If you picked a problem in a mechanical domain, you would immediately find that an ontology is not enough to perform that context-sensitive classification. For example, if you search healthBase now for "white noise", you find both pros ("calm baby") and cons ("corrupt measurement") -- impossible to do with an ontology alone.

A related problem with the choice of a specific domain is that the healthBase implementation has not narrowed the field of indexed data to medicine, so you may (as jokers have done) produce results that are amusing simply because they are so flagrantly not medical: I just searched for complications of Senator and the top result was "raise issue". This may actually point to some flaws in the IR besides those I've mentioned before, but stands out even more so for being non-medical.

Baby with the Bathwater

An earlier post on this blog was about the unfortunate recurrence of AI Winter, something which had hit the world pretty hard before I had made it out of my undergraduate work. To my dismay, the basic dynamic feeding it keeps on going, which is for the peddler of an AI-style technology (and NLP is certainly that) to over-market their own work, then live with the black mark on their reputation, and by extension, get people to write off the whole field. This is particularly damaging for those of us who make a living from it.

The reality of the healthBase technology is that it provides lots of useful results, and if you were earnestly seeking some background on a medical-related search -- no, not a replacement for actual medical advise -- you can get that information there. But, because at launch they had a system with far too many bad results, combined with the blogosphere's understandable joy at having a laugh, it turned into bad PR for the field as a whole. My dismay is all the greater for knowing that the company had already identified every one of these problems in 2007.

I'm completely certain that Information Retrieval will be a useful public-facing tool in the near future. I think NetBase had a window of opportunity to get there before anyone else, but in 2009, you can sense the Wolframs and the Bings and the Google Squareds circling the goal. The surest bet now is that whoever gets there first, they'll have an extremely short wait before there's company.

Natural Language Processing

Sometime long ago, the grunts of some species of ape began to take on a new character, enabling the animals to communicate in ways more powerful than any species had before. This surely took place gradually, and surely changed the apes as they changed this new thing they had, which we now call language. It was born around campfires and on hunts, in reference to children, prey, crops, predators, friends, enemies, and lovers. Over a span of hundreds of thousands of years, it grew up on six continents, in many different forms. It existed in two forms: In the mind, and as a spoken medium, formed in the mouth of a speaker, carried through the air as sound, and received in the eardrum of a listener.

Just a few thousand years ago, and in particular locations, humans began to write language. Just a few decades ago, humans began to encode written language so it could be stored and manipulated by digital computers. It didn't take long for people to realize that the kinds of tricks we can all perform, almost effortlessly, in crafting and understanding language are magnificently difficult to engineer in a machine.

Workers in Natural Language Processing (which also goes by the alias Computational Linguistics, among other names) have grappled with the difficulty of this subject matter. Theoretical and intellectual curiosity has driven research, as have governmental and commercial enterprises. The history of the field has been marked by optimism, setbacks, hype, successes, more optimism, more setbacks, more hype, and the occasional white lie.

The Internet, wireless communication, and portable computing are going to stimulate more interest in and need for NLP. If the past is any guide, there'll be a steady stream of optimism, setbacks, hype, and hopefully some successes. This blog will track NLP's ups and downs through 2008 and in the years to come.