How can I use php to categorise different keywords together for example to consider shoes, boots, nike, etc in the similar categories.
Any code would be appreciated.
(Warning: a fundamentalist approach) Look at MIT Reality Commons [0] and OpenCyc [1], [2]. These are two open databases of common sense. Make several searches by categories you're interested in. You'll get some related terms for each category. Put it in a fast database of your liking, and you're set.
Also, various SEO people like to create clouds of related keywords in meta tags of relevant pages. Take a look at source of several such pages, extract and filter keywords.
Related
I was going through this slide. I'm getting little difficulty in understanding the approach.
My two queries are:
How does Solr maintain schema of semi-structured document like
resumes (such as Name, Skills, Education etc)
Can Apache TIKA extract the section wise information from PDFs? Since every resume would have dissimilar sections, how do I define a
common schema of entities?
You define the schema, so that you get the fields you expect and can search in the different fields based on what kind of queries you want to do. You can lump any unknown (i.e. where you're not sure about where it belongs) values into a common search field and rank that field lower.
You'll have to parse the response from Tika (or a different PDF / docx parser) yourself. Just using Tika by itself will not give you an automagically structured response tuned to the problem you're trying to solve. There will be a lot of manual parsing and trying to make sense of what is what from the uploaded document, and then inserting the relevant data into the relevant field.
We did many implementations using solr and elastic search.
And got two challenges
defining schema and more specific getting document to given schema
Then expanding search terms to more accurate and useful match. Solr, Elastic can match which they get from content, but not beyond that content.
You need to use Resume Parser like www.rchilli.com, Sovrn, daxtra, hireability or any others and use their output and map to your schema. Best part is you get access to taxonomies to enhance your content is solr.
You can use any one based on your budget and needs. But for us RChilli worked best.
Let me know if you need any further help.
I am trying to parse entities from web pages that contain a time, a place, and a name. I read a little about natural language processing, and entity extraction, but I am not sure if I am heading down the wrong path, so I am asking here.
I haven't started implementing anything yet, so if certain open source libraries are only suitable for a specific language, that is ok.
A lot of times the data would not be found in sentences, but instead in html structures like lists (e.g. 2013-02-01 - Name of Event - Arena Name).
The structure of the webpages will be vastly different (some might use lists, some might put them in a table, etc.).
What topics can I research to learn more about how to achieve this?
Are there any open source libraries that take into account the structure of html when doing entity extraction?
Would extracting these (name, time, place) entities from html be better (or even possible) with machine vision where the CSS styling might make it easier to differentiate important parts (name, time, location) of the unstructured text?
Any guidance on topics/open source projects that I can research would help I think.
Many programming languages have external libraries that generate canonical date-stamps from various formats (e.g. in Java, using the SimpleDateFormat). As you say, the structure of the web-pages will be vastly different, but date can be expressed using a small number of variations only, so writing down the regular expressiongs for a few (let's say, half-a-dozen) formats will enable extraction of dates from most, if not all, HTML pages.
Extraction of places and names is harder, however. This is where natural language processing will have to come in. What you are looking for is a Named Entity Recognition system. One of the best open source NER systems is the Standford NER. Before using, you should check out their online demo. The demo has three classifiers (for English) that you can choose from. For most of my tasks, I find their english.all.3class.distsim classifier to be quite accurate.
Note that an NER performs well when the places and names you extract are occurring in sentences. If they are going to occur in HTML labels, this approach is probably not going to be very helpful.
I have a PDF file. And I want to search for names there.
How can I open the PDF and get all its text with Ruby?
Are there are any algorithms to find names?
What should I use as a search engine: Sphinx or something simpler (just LIKE sql queries)?
To find proper names in unstructured text, the technical name for the problem you are trying to solve is Named Entity Recognition or Named Entity Extraction. There are a number of different natural language toolkits and research papers which implement various algorithms to try to solve this problem. None of them will get perfect accuracy, but it may be good enough for your needs. I haven't tried it myself but the web page for Stanford Named Entity Recognizer has a link for Ruby Bindings.
Tough question. These domains remain in the research area of semantic web. I can only suggest some tracks but would be curious to know your definite choice.
I'd use pdf-reader: https://github.com/yob/pdf-reader
You could use a Bloom Filter matching some dictionary. You'd assume that words not matching the dictionary are names... Not always realistic but it's a first approach.
To get more names, you could check the words beginning with a capital letter (not great but we keep on finding some basic approaches). Some potential resource: http://snippets.dzone.com/posts/show/4235
For your search engine, the two main choices using Rails are Sphinx and SolR.
Hope this helps!
I'm not talking about HTML tags, but tags used to describe blog posts, or youtube videos or questions on this site.
If I was crawling just a single website, I'd just use an xpath to extract the tag out, or even a regex if it's simple. But I'd like to be able to throw any web page at my extract_tags() function and get the tags listed.
I can imagine using some simple heuristics, like finding all HTML elements with id or class of 'tag', etc. However, this is pretty brittle and will probably fail for a huge number of web pages. What approach do you guys recommend for this problem?
Also, I'm aware of Zemanta and Open Calais, which both have ways to guess the tags for a piece of text, but that's not really the same as extracting tags real humans have already chosen. But I would still love to hear about any other services/APIs to guess the tags in a document.
EDIT: Just to be clear, a solution that already works for this would be great. But I'm guessing there's no open-source software that already does this, so I really just want to hear from people about possible approaches that could work for most cases. It need not be perfect.
EDIT2: For people suggesting a general solution that usually works is impossible, and that I must write custom scrapers for each website/engine, consider the arc90 readability tool. This tool is able to extract the article text for any given article on the web with surprising accuracy, using some sort of heuristic algorithm I believe. I have yet to dig into their approach, but it fits into a bookmarklet and does not seem too involved. I understand that extracting an article is probably simpler than extracting tags, but it should serve as an example of what's possible.
Systems like the arc90 example you give work by looking at things like the tag/text ratios and other heuristics. There is sufficent difference between the text content of the pages and the surrounding ads/menus etc. Other examples include tools that scrape emails or addresses. Here there are patterns that can be detected, locations that can be recognized. In the case of tags though you don't have much to help you uniqely distinguish a tag from normal text, its just a word or phrase like any other piece of text. A list of tags in a sidebar is very hard to distinguish from a navigation menu.
Some blogs like tumblr do have tags whose urls have the word "tagged" in them that you could use. Wordpress similarly has ".../tag/..." type urls for tags. Solutions like this would work for a large number of blogs independent of their individual page layout but they won't work everywhere.
If the sources expose their data as a feed (RSS/Atom) then you may be able to get the tags (or labels/categories/topics etc.) from this structured data.
Another option is to parse each web page and look for for tags formatted according to the rel=tag microformat.
Damn, was just going to suggest Open Calais. There's going to be no "great" way to do this. If you have some target platforms in mind, you could sniff for Wordpress, then see their link structure, and again for Flickr...
I think your only option is to write custom scripts for each site. To make things easier though you could look at AlchemyApi. They have simlar entity extraction capabilities as OpenCalais but they also have a "Structured Content Scraping" product which makes it a lot easier than writing xpaths by using simple visual constraints to identify pieces of a web page.
This is impossible because there isn't a well know, followed specification. Even different versions of the same engine could create different outputs - hey, using Wordpress a user can create his own markup.
If you're really interested in doing something like this, you should know it's going to be a real time consuming and ongoing project: you're going to create a lib that detects which "engine" is being used in a page, and parse it. If you can't detect a page for some reason, you create new rules to parse and move on.
I know this isn't the answer you're looking for, but I really can't see another option. I'm into Python, so I would use Scrapy for this since it's a complete framework for scraping: it's complete, well documented and really extensible.
Try making a Yahoo Pipe and running the source pages through the Term Extractor module. It may or may not give great results, but it's worth a try. Note - enable the V2 engine.
Looking at arc90 it seems they are also asking publishers to use semantically meaningful mark-up [see https://www.readability.com/publishers/guidelines/#view-exampleGuidelines] so they can parse it rather easily, but presumably they must either have developed a generic rules such as #dunelmtech suggested tag/text ratios, which can work with article detection, or they might be using with a combination of some text-segmentation algorithms (from Natural Language Processing field) such as TextTiler and C99 which could be quite usefull for article detection - see http://morphadorner.northwestern.edu/morphadorner/textsegmenter/ and google for more info on both [published in academic literature - google scholar].
It seems that, however, to detect "tags" as you required is a difficult problem (for already mentioned reasons in comments above). One approach I would try out would be to use one of the text-segmentation (C99 or TextTiler) algorithms to detect article start/end and then look for DIV's / SPAN's / ULs with CLASS & ID attributes containing ..tag.. in them, since in terms of page-layout's tags tend to be generally underneath the article and just above the comment feed this might work surprisingly well.
Anyway, would be interesting to see whether you got somewhere with the tag detection.
Martin
EDIT: I just found something that might really be helpfull. The algorithm is called VIPS [see: http://www.zjucadcg.cn/dengcai/VIPS/VIPS.html] and stands for Vision Based Page Segmentation. It is based on the idea that page content can be visually split into sections. Compared with DOM based methods, the segments obtained by VIPS are much more semantically aggregated. Noisy information, such as navigation, advertisement, and decoration can be easily removed because they are often placed in certain positions of a page. This could help you detect the tag block quite accurately!
there is a term extractor module in Drupal. (http://drupal.org/project/extractor) but it's only for Drupal 6.
Does a tool exist that can parse text and output that text, hyper-linked to Wikipedia entries for words of interest?
For example, I'd like a tool that could turn something like:
The most popular search algorithm on a
sorted list is the binary search.
Into:
The most popular search algorithm on a
sorted list is the binary search.
It would be wonderful if Wikipedia had an API which would do this since they would be best equipped to determine what "words of interests" are.
In my example I simply linked all combinations which linked directly to an entry except for The and most.
There is a tool that does exactly what you're asking for.
http: //wikify.appointment.at/
It's not perfect, but it works.
You have two separate problems to solve here:
Deciding which words should be linked
Determining if there's a suitable entry to link these words to
Now, (2) is simpler, though it's also somewhat problematic. Wikipedia seems to have an API that allows you to gather data efficiently, and they also allow "screen scraping". But there's a problem with disambiguation - sometimes you might hit not the entry you wanted. For example, python links to a disambiguation page, as it can be a programming language, a snake and a couple of other things.
(1) Is much harder, though. You can take the "simple approach" and attempt to find links for all non-trivial nouns (or even noun/adjective pairs). Non-trivial here means omitting words like "fiend, word, computer" etc.
But This would result in a plethora of links, which isn't convenient to read. It's really up to you to decide what's interesting in the text, and this depends a lot on the text itself. In an article for professional programmers, do you really want to link to "search algorithm" every time? But for beginners, perhaps you do.
To conclude, I strongly doubt there's a single general-purpose tool that will do the trick for you. But you surely have all the options at your hand, and something need-specific can be coded without too much effort.
Silviu Cucerzan of Microsoft Research tackled this problem. Well, not the problem of inserting the links, but the general issue of determining what entities are being mentioned in a some piece of text. Fortunately for you, he used Wikipedia articles as his set of entities. His paper, "Large-Scale Named Entity Disambiguation Based on Wikipedia Data", is available on his website. Direct link: pdf.