For an application that we built, we are using a simple statistical model for word prediction (like Google Autocomplete) to guide search.
It uses a sequence of ngrams gathered from a large corpus of relevant text documents. By considering the previous N-1 words, it suggests the 5 most likely "next words" in descending order of probability, using Katz back-off.
We would like to extend this to predict phrases (multiple words) instead of a single word. However, when we are predicting a phrase, we would prefer not to display its prefixes.
For example, consider the input the cat.
In this case we would like to make predictions like the cat in the hat, but not the cat in & not the cat in the.
Assumptions:
We do not have access to past search statistics
We do not have tagged text data (for instance, we do not know the parts of speech)
What is a typical way to make these kinds of multi-word predictions? We've tried multiplicative and additive weighting of longer phrases, but our weights are arbitrary and overfit to our tests.
For this question, you need to define what it is you consider to be a valid completion -- then it should be possible to come up with a solution.
In the example you've given, "the cat in the hat" is much better than "the cat in the". I could interpret this as, "it should end with a noun" or "it shouldn't end with overly common words".
You've restricted the use of "tagged text data" but you could use a pretrained model, (e.g. NLTK, spacy, StanfordNLP) to guess the parts of speech and make an attempt to restrict predictions to only complete noun-phrases (or sequence ending in noun). Note that you would not necessarily need to tag all documents fed into the model, but only those phrases you're keeping in your autocomplete db.
Alternately, you could avoid completions that end in stopwords (or very high frequency words). Both "in" and "the" are words that occur in almost all English documents, so you could experimentally find a frequency cutoff (can't end in a word that occurs in more than 50% of documents) that help you filter. You could also look at phrases -- if the end of the phrase is drastically more common as a shorter phrase, then it doesn't make sense to tag it on, as the user could come up with it on their own.
Ultimately, you could create a labeled set of good and bad instances and try to create a supervised re-ranker based on word features -- both ideas above could be strong features in a supervised model (document frequency = 2, pos tag = 1). This is typically how search engines with data can do it. Note that you don't need search statistics or users for this, just a willingness to label the top-5 completions for a few hundred queries. Building a formal evaluation (that can be run in an automated manner) would probably help when trying to improve the system in the future. Any time you observe a bad completion, you could add it to the database and do a few labels -- over time, a supervised approach would get better.
I've been working with ElasticSearch within an existing code base for a few days, so I expect that the answer is easy once I know what I'm doing. I want to extend a search to yield the same results when I search with a compound word, like "eyewitness", or its component words separated by a whitespace, like "eye witness".
For example, I have a catalog of toy cars that includes both "firetruck" toys and "fire truck" toys. I would like to ensure that if someone searched on either of these terms, the results would include both the "firetruck" and the "fire truck" entries.
I attempted to do this at first with the "fuzziness" of a match, hoping that "fire truck" would be considered one transform away from "firetruck", but that does not work: ES fuzziness is per-word and will not add or remove whitespace characters as a valid transformation.
I know that I could do some brute-forcing before generating the query by trying to come up with additional search terms by breaking big words into smaller words and also joining smaller words into bigger words and checking all of them against a dictionary, but that falls apart pretty quickly when "fuzziness" and proper names are part of the task.
It seems like this is exactly the kind of thing that ES should do well, and that I simply don't have the right vocabulary yet for searching for the solution.
Thanks, everyone.
there are two things you could could do:
you could split words into their compounds, i.e. firetruck would be split into two tokens fire and truck, see here
you could use n-grams, i.e. for 4 grams the original firetruck get split into the tokens fire, iret, retr, etru, truc, ruck. In queries, the scoring function helps you ending up with pretty decent results. Check out this.
Always remember to do the same tokenization on both the analysis and the query side.
I would start with the ngrams and if that is not good enough you should go with the compounds and split them yourself - but that's a lot of work depending on the vocabulary you have under consideration.
hope the concepts and the links help, fricke
I am writing a text parser which should extract features from product descriptions.
Eg:
text = "Canon EOS 7D Mark II Digital SLR Camera with 18-135mm IS STM Lens"
features = extract(text)
print features
Brand: Canon
Model: EOS 7D
....
The way I do this is by training the system with structured data and coming up with an inverted index which can map a term to a feature. This works mostly well.
When the text contains measurements like 50ml, or 2kg, the inverted index will say 2kg -> Size and 50ml -> Size for eg.
The problem here is that, when I get a value which I haven't seen before, like 13ml, it won't be processed. But since the patterns matches to a size, we could tag it as size.
I was thinking to solve this problem by preprocessing the tokens that I get from the text and look for patterns that I know. So when new patterns are identified, that has to be added to the preprocessing.
I was wondering, is this the best way to go about this? Or is there a better way of doing this?
The age-old problem of unseen cases. You could train your scraper to grab any number-like characters preceding certain suffixes (ml, kg, etc) and treat those as size. The problem with this is typos and other poorly formatted texts could enter into your structure data. There is no right answer for how to handle values you haven't seen before - you'll either have to QC them individually, or have rules around them. This is dependent on your dataset.
As far as identifying patterns, you'll either have to manually enter them, or manually classify a lot of records and let the algorithm learn them. Not sure that's very helpful, but a lot of this is very dependent on your data.
If you have a training data like this:
word label
10ml size-valume
20kg size-weight
etc...
you could train a classifier based on character n-grams and that would detect that ml is size-volume even if it sees a 11-ml or ml11 etc. you should also convert the numbers into a single number (e.g. 0) so that 11-ml is seen as 0-ml before feature extraction.
For that you'll need a preprocessing module and also a large training sample. For feature extraction you can use scikit-learn's character n-grams and also SVM.
I was looking around some puzzles online to improve my knowledge on algorithms...
I came upon below question:
"You have a sentence with several words with spaces remove and words having their character order shuffled. You have a dictionary. Write an algorithm to produce the sentence back with spaces and words with normal character order."
I do not know what is good way to solve this.
I am new to algorithms but just looking at problem I think I would make program do what an intellectual mind would do.
Here is something I can think of:
-First find out manually common short english words from dictionary like "is" "the" "if" etc and put in dataset-1.
-Then find out permutation of words in dataset1 (eg "si", "eht" or "eth" or "fi") and put in dataset-2
-then find out from input sentense what character sequence matches the words of dataset2 and put them in dataset-3 and insert space in input sentence instead of those found.
-for rest of the words i would perform permutations to find out word from dictionary.
I am newbie to algorithms...is it a bad solution?
this seems like a perfectly fine solution,
In general there are 2 parameters for judging an algorithm.
correctness - does the algorithm provide the correct answer.
resources - the time or storage size needed to provide an answer.
usually there is a tradeoff between these two parameters.
so for example the size of your dictionary dictates what scrambled sentences you may
reconstruct, giving you a correct answer for more inputs,
however the whole searching process would take longer and would require more storage.
The hard part of the problem you presented is the fact that you need to compute permutations, and there are a LOT of them.
so checking them all is expensive, a good approach would be to do what you suggested, create a small subset of commonly used words and check them first, that way the average case is better.
note: just saying that you check the permutation/search is ok, but in the end you would need to specify the exact way of doing that.
currently what you wrote is an idea for an algorithm but it would not allow you to take a given input and mechanically work out the output.
Actually, might be wise to start by partitioning the dictionary by word length.
Then try to find the largest words that can be made using the letters avaliable, instead of finding the smallest ones. Short words are more common and thus will be harder to narrow down. IE: is it really "If" or "fig".
Then for each word length w, you can proceed w characters at a time.
There are still a lot of possible combinations though, simply because you found a valid word, doesn't mean it's the right word. Once you've gone through all the substrings, of which there should be something like O(c^4*d) where d is the number of words in the dictionary and c is the number of characters in the sentence. Practically speaking if the dictionary is sorted by word length, it'll be a fair bit less than that. Then you have to take the valid words, and figure out an ordering that works, so that all characters are used. There might be multiple solutions.
We were set an algorithm problem in class today, as a "if you figure out a solution you don't have to do this subject". SO of course, we all thought we will give it a go.
Basically, we were provided a DB of 100 words and 10 categories. There is no match between either the words or the categories. So its basically a list of 100 words, and 10 categories.
We have to "place" the words into the correct category - that is, we have to "figure out" how to put the words into the correct category. Thus, we must "understand" the word, and then put it in the most appropriate category algorthmically.
i.e. one of the words is "fishing" the category "sport" --> so this would go into this category. There is some overlap between words and categories such that some words could go into more than one category.
If we figure it out, we have to increase the sample size and the person with the "best" matching % wins.
Does anyone have ANY idea how to start something like this? Or any resources ? Preferably in C#?
Even a keyword DB or something might be helpful ? Anyone know of any free ones?
First of all you need sample text to analyze, to get the relationship of words.
A categorization with latent semantic analysis is described in Latent Semantic Analysis approaches to categorization.
A different approach would be naive bayes text categorization. Sample text with the assigned category are needed. In a learning step the program learns the different categories and the likelihood that a word occurs in a text assigned to a category, see bayes spam filtering. I don't know how well that works with single words.
Really poor answer (demonstrates no "understanding") - but as a crazy stab you could hit google (through code) for (for example) "+Fishing +Sport", "+Fishing +Cooking" etc (i.e. cross join each word and category) - and let the google fight win! i.e. the combination with the most "hits" gets chosen...
For example (results first):
weather: fish
sport: ball
weather: hat
fashion: trousers
weather: snowball
weather: tornado
With code (TODO: add threading ;-p):
static void Main() {
string[] words = { "fish", "ball", "hat", "trousers", "snowball","tornado" };
string[] categories = { "sport", "fashion", "weather" };
using(WebClient client = new WebClient()){
foreach(string word in words) {
var bestCategory = categories.OrderByDescending(
cat => Rank(client, word, cat)).First();
Console.WriteLine("{0}: {1}", bestCategory, word);
}
}
}
static int Rank(WebClient client, string word, string category) {
string s = client.DownloadString("http://www.google.com/search?q=%2B" +
Uri.EscapeDataString(word) + "+%2B" +
Uri.EscapeDataString(category));
var match = Regex.Match(s, #"of about \<b\>([0-9,]+)\</b\>");
int rank = match.Success ? int.Parse(match.Groups[1].Value, NumberStyles.Any) : 0;
Debug.WriteLine(string.Format("\t{0} / {1} : {2}", word, category, rank));
return rank;
}
Maybe you are all making this too hard.
Obviously, you need an external reference of some sort to rank the probability that X is in category Y. Is it possible that he's testing your "out of the box" thinking and that YOU could be the external reference? That is, the algorithm is a simple matter of running through each category and each word and asking YOU (or whoever sits at the terminal) whether word X is in the displayed category Y. There are a few simple variations on this theme but they all involve blowing past the Gordian knot by simply cutting it.
Or not...depends on the teacher.
So it seems you have a couple options here, but for the most part I think if you want accurate data you are going to need to use some outside help. Two options that I can think of would be to make use of a dictionary search, or crowd sourcing.
In regards to a dictionary search, you could just go through the database, query it and parse the results to see if one of the category names is displayed on the page. For example, if you search "red" you will find "color" on the page and likewise, searching for "fishing" returns "sport" on the page.
Another, slightly more outside the box option would be to make use of crowd sourcing, consider the following:
Start by more or less randomly assigning name-value pairs.
Output the results.
Load the results up on Amazon Mechanical Turk (AMT) to get feedback from humans on how well the pairs work.
Input the results of the AMT evaluation back into the system along with the random assignments.
If everything was approved, then we are done.
Otherwise, retain the correct hits and process them to see if any pattern can be established, generate a new set of name-value pairs.
Return to step 3.
Granted this would entail some financial outlay, but it might also be one of the simplest and accurate versions of the data you are going get on a fairly easy basis.
You could do a custom algorithm to work specifically on that data, for instance words ending in 'ing' are verbs (present participle) and could be sports.
Create a set of categorization rules like the one above and see how high an accuracy you get.
EDIT:
Steal the wikipedia database (it's free anyway) and get the list of articles under each of your ten categories. Count the occurrences of each of your 100 words in all the articles under each category, and the category with the highest 'keyword density' of that word (e.g. fishing) wins.
This sounds like you could use some sort of Bayesian classification as it is used in spam filtering. But this would still require "external data" in the form of some sort of text base that provides context.
Without that, the problem is impossible to solve. It's not an algorithm problem, it's an AI problem. But even AI (and natural intelligence as well, for that matter) needs some sort of input to learn from.
I suspect that the professor is giving you an impossible problem to make you understand at what different levels you can think about a problem.
The key question here is: who decides what a "correct" classification is? What is this decision based on? How could this decision be reproduced programmatically, and what input data would it need?
I am assuming that the problem allows using external data, because otherwise I cannot conceive of a way to deduce the meaning from words algorithmically.
Maybe something could be done with a thesaurus database, and looking for minimal distances between 'word' words and 'category' words?
Fire this teacher.
The only solution to this problem is to already have the solution to the problem. Ie. you need a table of keywords and categories to build your code that puts keywords into categories.
Unless, as you suggest, you add a system which "understands" english. This is the person sitting in front of the computer, or an expert system.
If you're building an expert system and doesn't even know it, the teacher is not good at giving problems.
Google is forbidden, but they have almost a perfect solution - Google Sets.
Because you need to unterstand the semantics of the words you need external datasources. You could try using WordNet. Or you could maybe try using Wikipedia - find the page for every word (or maybe only for the categories) and look for other words appearing on the page or linked pages.
Yeah I'd go for the wordnet approach.
Check this tutorial on WordNet-based semantic similarity measurement. You can query Wordnet online at princeton.edu (google it) so it should be relatively easy to code a solution for your problem.
Hope this helps,
X.
Interesting problem. What you're looking at is word classification. While you can learn and use traditional information retrieval methods like LSA and categorization based on such - I'm not sure if that is your intent (if it is, then do so by all means! :)
Since you say you can use external data, I would suggest using wordnet and its link between words. For instance, using wordnet,
# S: (n) **fishing**, sportfishing (the act of someone who fishes as a diversion)
* direct hypernym / inherited hypernym / sister term
o S: (n) **outdoor sport, field sport** (a sport that is played outdoors)
+ direct hypernym / inherited hypernym / sister term
# S: (n) **sport**, athletics
(an active diversion requiring physical exertion and competition)
What we see here is a list of relationships between words. The term fishing relates to outdoor sport, which relates to sport.
Now, if you get the drift - it is possible to use this relationship to compute a probability of classifying "fishing" to "sport" - say, based on the linear distance of the word-chain, or number of occurrences, et al. (should be trivial to find resources on how to construct similarity measures using wordnet. when the prof says "not to use google", I assume he means programatically and not as a means to get information to read up on!)
As for C# with wordnet - how about http://opensource.ebswift.com/WordNet.Net/
My first thought would be to leverage external data. Write a program that google-searches each word, and takes the 'category' that appears first/highest in the search results :)
That might be considered cheating, though.
Well, you can't use Google, but you CAN use Yahoo, Ask, Bing, Ding, Dong, Kong...
I would do a few passes. First query the 100 words against 2-3 search engines, grab the first y resulting articles (y being a threshold to experiment with. 5 is a good start I think) and scan the text. In particular I"ll search for the 10 categories. If a category appears more than x time (x again being some threshold you need to experiment with) its a match.
Based on that x threshold (ie how many times a category appears in the text) and how may of the top y pages it appears in you can assign a weigh to a word-category pair.
for better accuracy you can then do another pass with those non-google search engines with the word-category pair (with a AND relationship) and apply the number of resulting pages to the weight of that pair. Them simply assume the word-category pair with highest weight is the right one (assuming you'll even have more than one option). You can also multi assign a word to a multiple category if the weights are close enough (z threshold maybe).
Based on that you can introduce any number of words and any number of categories. And You'll win your challenge.
I also think this method is good to evaluate the weight of potential adwords in advertising. but that's another topic....
Good luck
Harel
Use (either online, or download) WordNet, and find the number of relationships you have to follow between words and each category.
Use an existing categorized large data set such as RCV1 to train your system of choice. You could do worse then to start reading existing research and benchmarks.
Appart from Google there exist other 'encyclopedic" datasets you can build of, some of them hosted as public data sets on Amazon Web Services, such as a complete snapshot of the English language Wikipedia.
Be creative. There is other data out there besides Google.
My attempt would be to use the toolset of CRM114 to provide a way to analyze a big corpus of text. Then you can utilize the matchings from it to give a guess.
My naive approach:
Create a huge text file like this (read the article for inspiration)
For every word, scan the text and whenever you match that word, count the 'categories' that appear in N (maximum, aka radio) positions left and right of it.
The word is likely to belong in the category with the greatest counter.
Scrape delicious.com and search for each word, looking at collective tag counts, etc.
Not much more I can say about that, but delicious is old, huge, incredibly-heavily tagged and contains a wealth of current relevant semantic information to draw from. It would be very easy to build a semantics database this way, using your word list as a basis from scraping.
The knowledge is in the tags.
As you don't need to attend the subject when you solve this 'riddle' it's not supposed to be easy I think.
Nevertheless I would do something like this (told in a very simplistic way)
Build up a Neuronal Network which you give some input (a (e)book, some (e)books)
=> no google needed
this network classifies words (Neural networks are great for 'unsure' classification). I think you may simply know which word belongs to which category because of the occurences in the text. ('fishing' is likely to be mentioned near 'sports').
After some training of the neural network it should "link" you the words to the categories.
You might be able to put use the WordNet database, create some metric to determine how closely linked two words (the word and the category) are and then choose the best category to put the word in.
You could implement a learning algorithm to do this using a monte carlo method and human feedback. Have the system randomly categorize words, then ask you to vote them as "match" or "not match." If it matches, the word is categorized and can be eliminated. If not, the system excludes it from that category in future iterations since it knows it doesn't belong there. This will get very accurate results.
This will work for the 100 word problem fairly easily. For the larger problem, you could combine this with educated guessing to make the process work faster. Here, as many people above have mentioned, you will need external sources. The google method would probably work the best, since google's already done a ton of work on it, but barring that you could, for example, pull data from your facebook account using the facebook apis and try to figure out which words are statistically more likely to appear with previously categorized words.
Either way, though, this cannot be done without some kind of external input that at some point came from a human. Unless you want to be cheeky and, for example, define the categories by some serialized value contained in the ascii text for the name :P