NLP Challenge: Find semantically related terms over a large vocabulary (1M)?

This doesn't make much sense....

From the website, here are some example "documents":

  roger heavens trinity cricketers willie sugg early cambridge cricket giles phillips cambridge university cricket

  roger hornetttom pridmore 020 7399 4270 collins stewartalan geeves 020 7523 8800 buchanan communicationscharles ryland /isabel podda 020 7466 5000

  roger hubbold aig .1 force-field navigation

Now, what is the point in trying to generate any kind of "meaning" from those documents if they consist of completely meaningless gibberish?

As I was reading this challenge, I immediately thought of spam filtering / youtube comment classification ("smartness" classification) / etc as a potential useful application of this technology.

For example if each "document" is a youtube comment, then in theory you could write an algorithm to examine each comment and output a "smartness guess" for each. Then you (as in, you personally, by hand) would look at the results and specify your own "smartness rating" for a few comments. Then you'd run an algorithm to look at the difference between your specified "smartness rating" and the "smartness guess". Then, using that difference, it would tweak the settings in the original algorithm until it outputs a "smartness guess" that more closely fits your "smartness rating". If you repeat that process enough times, and your original algorithm has enough modifiers to tweak, then you might wind up with an algorithm that can make a pretty good guess about whether any given youtube comment is retarded or not.

And that's just one example of a practical application for this kind of thing.

That said, if the input "documents" are completely and utterly meaningless, then there does not seem to be any point in trying to build "meaning" from those inputs. (Garbage in, garbage out.)

That's somewhat similar to my second mini-project of my NLP course (that I just delivered like one week ago).

What we had to do was, first to gather a large corpus (in this case) of portuguese. Then, produce the frequencies of each unigram and bigram. Then, process those frequencies and output the top 10 and bottom 10 collocations of the corpus. Collocation definition from wikipedia: "Within the area of corpus linguistics, collocation defines a sequence of words or terms that co-occur more often than would be expected by chance".

The method used to calculate the collocations was based on a chi-square test. The values with higher chi-square were the most probable collocations (if above the critical value that would reject the null hypothesis).

So, I actually have Ruby code done that I could change just a little bit for this, but there are probably more clever methods.

This sounds interesting but the main challenge here seems to be to define the question: What is a semantically related word. I can think of three classes - variations (singular/plural), synonyms (thesaurus), contextual (occur in the same sentence/paragraph). Once the definition of "semantically related" is established, it should be easy to find a clever algorithm to produce results. However, out of context the problem is a little bit boring - in order to find a good solution I'd like to know the real world problem the list of "semantically related" words is going to solve.

My first idea would be the following:

If I had enough time for the problem I would first use a morphological and syntactical analyzer to try (at least with a high probability) to determine the morphological structure of the words and I would tag them with their syntactical category (part of speech). So I would treat for example 'work' and 'worked' as being the same word. On the other hand I would treat 'can:aux' and 'can:noun' as different words.

Now I would completely eliminate all the words which are not nouns not adjectives and not verbs. (the, a, can:aux, may, etc...) These are 'asemantical' words.

Maybe eliminate extremely rare words also (with 1 or 2 occurences)

After this preprocessing now comes the semantical relatedness analysis.

A very primitive solution:

----

Compute the ten best B for A where the following is maximal:

f(AB) / (f(A) * f(B))

f can be the frequency. f(AB) can be the frequency of occuring together in a document or within a distance (like 5 words). But you can fine tune it: weight it with the actual distance.

You don't have to maintain the full matrix. As you go through the documents just maintain max 100 words for each word for which this measure is maximal, and in the end just take the 10 maximal from the 100.

----

A more sophisticated solution for the semantically relatedness analysis:

Let's put all the possible words into a 2 dimensional map with totally random x,y coordinates. Now as you go through the documents imagine that the words which occur near each other attract each other on the map so their distance is made *=0.8. They gravitate. Imagine that the mass of the word is its frequency: the distance is made smaller by small frequency words going towards the big frequency word a lot, and the a big frequency word goes just a litle bit. Just like gravitation. Of course time to time we change the scale: we blow the whole map up a bit to not make everything gravitate into one point. This algorithm is a 'global algorithm' in the sense that it takes into account indirect things like synonims which are semantically really related even though they not really occur in the same sentence. In the end determining the 10 nearest neighbours fast is easy: you can use a simple grid as 'space partitioning'.

Edit: A 2 dimensional map s not necessarily optimal, it might be too tight. I would also experiment with setting the map dimension to 3,4,5 etc...

I checked in a simple co-occurrence frequency based implementation for this here https://github.com/tgflynn/NLP-Challenge with a short discussion on my blog: http://cogniception.com/wp/.

Solution: http://groups.google.com/group/metaoptimize-challenge-discus...

Hmm. Without any thought given to computability: Rearrange the words of every document into alphabetical order, then sort the whole list of documents into alphabetical order. Now, start reading the documents, and for each one make it a tree with each word a leaf, BUT if there is already a tree with a fragment of the same phrase, then that tree is linked to as a node of the new tree.

So for the document 'christmas day' the tree

______d1______.

__/_________\__.

christmas___day

is formed. But for the document 'chistmas day special', the tree

_____d2______.

___/____\____.

d1_______special

is formed. Note: word order doesn't matter. For any given document, how will it know if there's already a tree that uses some of the same words? When a tree is created from a document, give it a hash for the full set of words, plus hashes for sub-strings. These can be stored in a big lookup table, so new documents know where to look for relevant trees (or -- use whatever solution is well established for this sort of problem). Might also need to algorithmically redefine older trees as we go along, ie. divide a tree into smaller phrases

For each leaf let it keep track of what trees its part of (including grandparents and above). Then to find the top-k, for each leaf object (if it's in the vocabulary), print out all the other leaves of all the trees its part of. Like

if leaf.__str__ == 'day':

___ for tree in leaf.trees

______ print tree.leaves

Then do a simple frequency count to find the top-k associated words.

(sorry for the n00b answer... IANACS)

One way of doing this:

1. Get some million tweets from twitter API (each tweet is exactly like what the article describes as a 'document')

2. After ignoring common words (e.g. 'a', 'an', 'the' ... ) in a tweet , start assigning some kind of relevance rank to all the other word pairs found in the tweet e.g. its likely that 'Cricket' and 'Sachin' (Or 'NBA' and <top NBA player) will both increase each other's relevance rank, WRT to each-other.

3. Process all the tweets like this, while maintaining the output of step 2 in a most suitable data structure. You would also need to start dropping word-pairs (to avoid having the problem of storing Million-C-2 words! ) based on some logic/heuristic.

4. If we have a good logic for having reasonably not-big storage (by avoiding the million-c-2 explosion), then what we have is at the end of processing: A simple look up of a million words, where each word has its 'top' max_allowed(k) semantic words.

PS: The most complex piece in the approach is to come out with a solution for dropping word-pairs (in step 3)

I will no bother with this challenge, since there is no a clear good solution. I would find more interesting working with semantically rich documents in which your work can be tested to measure the quality of your solution. That is, a good work must always gain enough recognition to be pursued.

Couple of approaches that I can think of from papers of which combining in some way would probably mean decent results. Matching left and right fragments of n length, adding POS tagging to the matching fragments and using wordnet relation closeness for terms that exist in wordnet.

I encourage anyone to use Extractiv (http://www.extractiv.com) for this challenge. We're rolling out an On-Demand (local document analysis similar to Calais) Semantic Conversion service that could be helpful.