Posts Tagged ‘semantic dictionary’

ABC’s of Semantic Dictionaries

14 Dec 2009

A semantic dictionary in essence consists of triples [ t , d , w ] , where t is a term, d is a semantic dimension, and w is a weight. Each triple says that the occurrence of a term t in a document constitutes a raw vote of w for dimension d being relevant to the document. For example, [ BRAD , Arts/People/Jolie,_Angelina , 0.12100 ] indicates that the occurrence of BRAD in a news story provides evidence that it might be about the movie celebrity Angelina Jolie. If it were conclusive evidence, the weight would be 1.00000, but we never expect any single term to be that definitive.

In building a dictionary for an application, we have to start with the dimensions. Do we have the kinds of dimensions to cover the target content, and are there enough dimensions to make the distinctions in content required by the application? For a patent information system, an Angelina dimension may not have much relevance, and even something more appropriate like Electrical Machinery may have to be divided up into multiple dimensions to support a reasonable level of granularity in indexing.

Given the dimensions, we next have to define the terms to go along with them. The target content we want to process will have a certain vocabulary, and our dictionary terms should try to encompass most of it. This can be tricky in a statistical approach because we need reasonably large samples of training data to make a particular term become associated with a particular dimension.

Weights are determined in large part by training data, but the distribution of those numbers are important. To begin with, not all weights should be the same, and generally, we want to see them spread out over the entire dynamic range available to us. Weights that are quite big or quite large have to be supported by more data that those in the middle range. Weights have to be balanced between dimensions, and there should be enough of them so that most terms are related to more than one dimension.

In theory, we could build a dictionary with just one weight in just one dimension for each term, but that would be in denial about the inherent ambiguity of language. So, we typically want a dictionary to be as big as possible, based on an appropriate amount of training data. To build the best possible dictionary requires much inspiration and much perspiration.

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How Many Semantic Dictionaries?

7 Dec 2009

Even in the world of print, one dictionary is often not enough. Just for English, for example, we can go to standard references like Webster’s Third New International, The American Heritage Dictionary of the English Language, or the Oxford English Dictionary, as well as more specialized lexicons. So how many semantic dictionaries do we really need?

That of course depends on the application. If we are in the situation where our target text data is extremely stable and requires only a general vocabulary, then we might get away with a single semantic dictionary based on a large sample of data processed quite carefully. On the Web, however, we have nothing of the sort, if you haven’t noticed lately.

A sophisticated dictionary that took weeks to build with hairy mathematical algorithms on a reasonable sample of training text may become obsolete overnight. That is not to say that sophisticated dictionaries are unhelpful; but in the merciless competition of the information marketplace, we probably need to be able to pop out a new semantic dictionary based on a gigabyte or more of text in just hours.

Given this kind of turnaround, why would anyone want to rely on a single semantic dictionary with its limited vocabulary and somewhat dated concepts? A new dictionary will of course involve a nontrivial upfront investment, but once a reliable source of tagged data is developed, actual dictionary building can be largely automated. That is the advantage of relying on statistical methods.

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Casino Royale

14 Sep 2009

In any statistical information system, one can never achieve absolute certainty. Every result is a kind of bet with the possibility of losing. For Semantic Signatures, however, this is more like playing blackjack than like playing roulette. Whether we imagine ourselves as the house or some hotshot card counter, we try our utmost to bend the odds in our favor.

When a given term occurs in a document, we know that there is a certain probability that the document is about a given topic. For example, THRILLER may relate to Michael Jackson or to some recent summer popcorn epic. Similarly MOONWALK may refer to Apollo XI or to a dance move. We would be rash to judge content just on the basis of a single term, but when multiple terms can corroborate each other, we do have a better bet.

The trick here is to able to set up a semantic dictionary so that we can always expect to find a reasonable number of terms in a target document that allow us to make that better bet. This requires careful balancing: we need enough semantic dimensions to be able to distinguish the different important kinds of content and enough terms for each dimension to put it into play. It is much like developing a diverse portfolio of investments to weather any shift in economic climate.

Most people will probably pass on building their own semantic dictionaries. It takes a tremendous amount of work to collect and filter the requisite text data to ground our dictionary weights and to massage all those numbers to get the maximum amount of usable information. But we want to get on the right side of the odds.

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Quality Assurance

21 Aug 2009

When is a semantic dictionary good? It really depends on the application, since more specialized content requires more specialized dictionary dimensions. Typically,  validation of a given application will involve extensive benchmark testing, often entailing human judgments of the effectiveness of particular statistical characterizations of content.

TextWise does all of this in its product development process, but one would not want to go through an elaboration validation procedure to test the consequences of every small change. As it turns out, there are quick statistical ways to check whether a change is likely to be good or bad. This is no substitute for actual detailed validation at some point, but it allows one to experiment with new ideas at a fairly low cost.

A digital photography metaphor is apt here. One cannot use statistics to identify a prize-winning shot, it is certainly possible to detect major problems without human judgments. For example, areas of maximally white pixels indicate blown highlights, which typically detract from the quality of an image. Similarly, problems with white balance, dynamic range, focus, and other conditions are also readily detectable.

With any huge data object like a semantic dictionary, it is difficult to construct a benchmark that will cover every aspect of it thoroughly. Statistical testing provides an overall sanity check on quality. Otherwise, one would just be buying and selling pigs in a poke.

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Des Dictionnaires Sémantiques Sont Très Tsoin-Tsoin?

15 Aug 2009

In our current effort to develop a French semantic dictionary, we ran across the word TSOIN in two stop lists posted on the Web. It was not in my old pocket Larousse, but an online French lexicon explained that it usually appeared in the doubled form “tsoin-tsoin.” Unfortunately, it had “no official definition.”

How can an expression common enough to be included on a stop list have no meaning? At this point, contextual semantics came in to save the day. Who really needs a normative model-driven definition? We could simply apply our standard structural methods to characterize where the expression occurs.

To begin with, there is a French Wikipedia tongue-in-cheek article about the African “tsoin-tsoin fly” that carries a lethargic disease that kills a person in about twenty or thirty years. A French rock band issued an album with “tsoin-tsoin” in its title. Several bloggers or forum posters have it in their user names.

So we can infer that “tsoin-tsoin” is probably not obscene. It seems to have some negative connotations like “slacker” or “lazy,” but in a somewhat positive way. A similar English adjective would be “laidback,” and one may perhaps even say “cool.”

Actually, our sample size is much too small to make any reliable definition yet, but with more examples of its occurrence,  we can eventually home in on a broadly accepted meaning. In a sense, this meaning is still being developed in popular usage; we are watching contextual semantics at work in real life.

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Why So Many Semantic Dictionaries?

10 Aug 2009

The current SemanticHacker API offers more than one semantic dictionary. Each one is crafted from a particular collection of categorized documents at a particular time. The choice of a dictionary depends on one’s target application. Ideally, that dictionary will be trained on categorized documents similar to the documents to be analyzed for content.

Currently, the two main types of dictionaries available in English come from the ODP conceptual hierarchy and the USPTO class hierarchy. The dimensions defined for these types have practically no overlap. The differences in language and vocabulary in training data are also huge, and these have major consequences in the dimensional weights computed for terms in the two dictionaries.

In theory, one could employ a USPTO dictionary in a general web application, but one then risks being unable to pick up on popular language and culture. You won’t find “lol” or “Brangelina” in any patent. Similarly, an ODP dictionary may be a bit thin for handling medical journal articles; it would be much better here to have a semantic dictionary trained specifically on medical language and vocabulary.

The cost of building a specialized dictionary varies, mostly due to the complicated legal, technical, and logistical process of collecting the proper training data. Once the data is obtained, however, the actual dictionary process is largely mechanical, although we do carry out extensive quality assessment to determine whether we are running under optimal dictionary building parameters.

With proper training data in hand, we can turn out a semantic dictionary of about 200,000 terms over 2,000 dimensions in only about a day. This turnaround is possible because of our reliance on statistical methods as opposed to more complicated mathematical modeling of other semantic approaches. It means that we could build new dictionaries fast enough to keep up with news cycles as short as one week, given the computational resources needed.

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A Final Look at Denotation and Connotation

7 Aug 2009

Some linguists believe that early language was always about specific entities–that is, denotational; this kind of reference then evolves into concepts, which are connotational. For example, a baby learns his or her particular meaning of MAMA, which then generalizes into MOTHERHOOD.

We can still see such evolution at work today. About two years ago, the term “Sarah Palin” was only denotational, but after the fall of 2008, it has now become quite connotational. Something similar might be said on the other side of the political spectrum about the term “Barack Obama.”

The whole process of turning denotation into connotation has been extensively studied and is better known as “branding.” Anyone who has ever written a resumé has had experience in doing it.

A semantic dictionary in fact trades on the natural kind of branding. Since we do statistical analyses of context to assign meaning, We may not yet be able to interpret a term for which we have only a small sample of occurrences. Give us a little time, though.

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What’s in a Name?

6 Aug 2009

Romeo Montague once noted that the semantic function of a name contrasts quite saliently with that of an ordinary word. Shakespeare didn’t quite put it that way, but it is a fact of language. Classic semanticist would frame it as a distinction between denotation (i.e. identification) versus connotation (i.e. description).

As it turns out, this difference can be seen even at the statistical level. Ordinary words with a little massaging have a frequency distribution best described as binomial; names are typically not binomial. That will have consequences for how we mine text data to create a semantic dictionary.

This is all a fine point, but the quality of a product is determined by many such fine points. None of our API competitors on the web bother with denotation and connotation, but it can really matter when you are processing data with many product designations.

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