Anybody have an example of a regular expression that matches for address, zip, or [city,state]?
Update:
Admittedly, this is a weak question because I don't have enough information regarding user behavior at this point to really qualify the parameters of the problem. Here is what I'm trying to do though:
Create a search function that depending on what information has been entered in chooses one of two divergent paths, the first being address proximity search and the second being organization name search.
It is proving a difficult problem to solve, so any input out there, besides .* (okay, okay I deserved that) would be much appreciated.
Check out geocoder (http://www.rubygeocoder.com/). It will get lat/long from text input. What you could do for your search is first try to match organization names, and then try to match locations.
Luckily google figure out how to do proximity searches a while ago
Related
When searching for something in Google, if you misspell a word (may be by mistake or may be when you really mean this non-dictionary word), Google says:
"Showing results for ..... Search instead for .......".
I am trying to figure out how this would work.
This basically means being able to find the closest dictionary word to the non-dictionary word entered. How does it work? One way I can guess is :
count no. of instances of each character and then scan dictionary to find a word with same no. of instances of each character (only with +-1 difference). But this will also return anagrams.
Is some kind of probabilistic model of any use here such as Markov etc. I don't understand Markov well enough to throw it around but just a very wild guess.
Any insights?
You're forgetting that google has a lot more information available to it then you do. They track when people type in a word, don't select a result, and then do another search shortly afterwards. They then use this information to suggest better searches for you.
See How does the Google "Did you mean?" Algorithm work? for a fuller explanation.
Note that this approach makes sense when you consider that Google aren't actually doing spell-checking. Instead, they are trying to work out what search term will give you the answer you are looking for. Obviously there is a lot of overlap between this and spell-checking, but it means they are not always trying to correct a search for, e.g., "Flickr".
When you search something which is related to other searches performed earlied closed to yours and got more results, google shows suggest on them.
We are sure that it is not spell checking but it shows what other people queried the related keywords.
To make matter more specific:
How to detect people names (seems like simple case of named entity extraction?)
How to detect addresses: my best guess - find postcode (regexes); country and town names and take some text around them.
As for phones, emails - they could be probably caught by various regexes + preprocessing
Don't care about education/working experience at this point
Reasoning:
In order to build a fulltext index on resumes all vulnerable information should be stripped out from them.
P.S. any 3rd party APIs/services won't do as a solution.
The problem you're interested in is information extraction from semi structured sources. http://en.wikipedia.org/wiki/Information_extraction
I think you should download a couple of research papers in this area to get a sense of what can be done and what can't.
I feel it can't be done by a machine.
Every other resume will have a different format and layout.
The best you can do is to design an internal format and manually copy every resume content in there. Or ask candidates to fill out your form (not many will bother).
I think that the problem should be broken up into two search domains:
Finding information relating to proper names
Finding information that is formulaic
Firstly the information relating to proper names could probably be best found by searching for items that are either grammatically important or significant. I.e. English capitalizes only the first word of the sentence and proper nouns. For the gramatical rules you could look for all of the words that have the first letter of the word capitalized and check it against a database that contains the word and the type [i.e. Bob - Name, Elon - Place, England - Place].
Secondly: Information that is formulaic. This is more about the email addresses, phone numbers, and physical addresses. All of these have a specific formats that don't change. Use a regex and use an algorithm to detect the quality of the matches.
Watch out:
The grammatical rules change based on language. German capitalizes EVERY noun. It might be best to detect the language of the document prior to applying your rules. Also, another issue with this [and my resume sometimes] is how it is designed. If the resume was designed with something other than a text editor [designer tools] the text may not line up, or be in a bitmap format.
TL;DR Version: NLP techniques can help you a lot.
I'm curious what the programming terms or methodology is used when Google shows you the "did you mean" link for a word that is made up of multiple words?
For example if I type in "redflower.jpg" It knows to break that up into Red Flower
Is there a common paradigm for doing that sort of operation? Would a Lucene search give you that?
thanks!
If google does not see a lot of matching results for reflowers.jpg, it might then try to cut the words in multiple words until it finds a lot of matching results.
It might also recognize the extension (.jpg), recognize the image extension and then try to find images with the similar name.
If I would have to make an algorithm like this, I would use an huge EXISTING database (either a dictionary or a search engine) and then try what I said in the beginning of my post.
Perhaps they could to look at what other people do when they have searched for redflowers.jpg? Maybe a number of people searched for "redflowers.jpg", didn't click on any links, and then searched for "Red Flower" and found some results worth clicking on.
Of course they would have to take into account that the queries are similar (contain matching strings), otherwise some strange results might appear.
This is silly, but I haven't found this information. If you have names of concepts and suitable references, just let me know.
I'd like to understand how should I validate a given named id for a generic entity, like, say, an email login, just like Yahoo, Google and Microsoft do.
I mean... If you do have an user named foo, trying to create foo2 will be denied, as it is likely to be someone trying to mislead users by using a fake id.
Coming to mind:
Levenshtein Distance
Hamming Distance
You're going to have to take a two pass approach.
The first is a potential RegEx expression to validate that the entity name meets your specifications as much as possible. For example, disallowing certain characters.
The second is to perform some type of fuzzy search during the name creation. This could be as simple as a LIKE '%value%' where clause or as complicated as using some type of full-text search and limiting hits to a certain relevance rating.
That said, I would guess the failure rate (both false positives and false negatives ) match would be high enough to justify not doing this.
Good luck.
What are the best algorithms for recognizing structured data on an HTML page?
For example Google will recognize the address of home/company in an email, and offers a map to this address.
A named-entity extraction framework such as GATE has at least tackled the information extraction problem for locations, assisted by a gazetteer of known places to help resolve common issues. Unless the pages were machine generated from a common source, you're going to find regular expressions a bit weak for the job.
If you have the markup proper—and not just the text from the page—I second the Beautiful Soup suggestion above. In particular, the address tag should provide the lowest of low-hanging fruit. Also look into the adr microformat. I'd only falll back to regexes if the first two didn't pull enough info or I didn't have the necessary data to look for the first two.
If you also have to handle international addresses, you're in for a world of headaches; international address formats are amazingly varied.
I'd guess that Google takes a two step approach to the problem (at least that's what I would do). First they use some fairly general search pattern to pick out everything that could be an address, and then they use their map database to look up that string and see if they get any matches. If they do it's probably an address if they don't it probably isn't. If you can use a map database in your code that will probably make your life easier.
Unless you can limit the geographic location of the addresses, I'm guessing that it's pretty much impossible to identify a string as an address just by parsing it, simply due to the huge variation of address formats used around the world.
Do not use regular expressions. Use an existing HTML parser, for example in Python I strongly recommend BeautifulSoup. Even if you use a regular expression to parse the HTML elements BeautifulSoup grabs.
If you do it with your own regexs, you not only have to worry about finding the data you require, you have to worry about things like invalid HTML, and lots of other very non-obvious problems you'll stumble over..
What you're asking is really quite a hard problem if you want to get it perfect. While a simple regexp will get it mostly right most of them time, writing one that will get it exactly right everytime is fiendishly hard. There are plenty of strange corner cases and in several cases there is no single unambiguous answer. Most web sites that I've seen to a pretty bad job handling all but the simplest URLs.
If you want to go down the regexp route your best bet is probably to check out the sourcecode of
http://metacpan.org/pod/Regexp::Common::URI::http
Again, regular expressions should do the trick.
Because of the wide variety of addresses, you can only guess if a string is an address or not by an expression like "(number), (name) Street|Boulevard|Main", etc
You can consider looking into some firefox extensions which aim to map addresses found in text to see how they work
You can check this USA extraction example http://code.google.com/p/graph-expression/wiki/USAAddressExtraction
It depends upon your requirement.
for email and contact details regex is more than enough.
For addresses regex alone will not help. Think about NLP(NER) & POS tagging.
For finding people related information you cant do anything without NER.
If you need information like paragraphs get the contents by using tags.