Has anybody come across or created a functionality comparison document between the various enterprise search options like Google Search Appliance, Fast ESP, Lucene?
Any helpful pointers or links much appreciated.
Strangely enough I have, sort of. It's so dependent on your use case and $$$ requirements though that such an open ended question is pretty useless. If you need XML indexing, with things like XPath type queries, then FAST is the only way to go. Trouble is, it costs &&&. If you don't have a lot of hardware or $$$ then you will never beat Lucene's searching ability. Lucene's biggest strength (other then it's insanely fst indexing/searching) is it's extensibility. If you have a couple developers willing to write their own parsers, tokenizers, and query optimizers then there are no limits to Lucene. But if you need a COTS solution with almost no upkeep then the Goggle Appliance is pretty hard to beat.
They have and it's very useful. One thing about FAST is that, they (MS) moved some of components to SharePoint and discontinued support.
http://www.lightcrest.com/site_media/pdfs/Search-Comparison_Solr-FAST.pdf
Related
This question has been asked in various ways before, but I'm wondering if people who have experience with automatic search term suggestion could offer advice on the most useful and efficient approaches. Here's the scenario:
I'm just starting on a website for a book that is a dictionary of terms (roughly 1,000 entries, with 300 word explanations on average), many of which are fairly obscure, and it is likely that many visitors to the site would not know how to spell the words. The publisher wants to make full-text search available for every entry. So, I'm hoping to implement a search engine with spelling correction. The main site will probably be done in a PHP framework (or possibly Django) with a MySQL database.
Can anyone with experience in this area give advice on the following:
With a set corpus of this nature, should I be using something like Lucene or Sphinx for the search engine?
As far as I can tell, neither of these has a built-in suggestion function. So it seems I will need to integrate one or more of the following. What are the advantages / disadvantages of:
Suggestion requests through Google's search API
A phonetic comparison algorithm like metaphone() in PHP
A spell checking system like Aspell
A simpler spelling script such as Peter Norvig's
A Levenshtein function
I'm concerned about the specificity of my corpus, and don't want Google to start suggesting things that have nothing to do with this book. I'm also not sure whether I should try to use both a metaphone comparison and a Levenshtein comparison, or some other combination of techniques to capture both typos and attempts at phonetic spelling.
You might want to consider Apache Solr, which is a web service encapsulation of Lucene, and runs in a J2EE container like Tomcat. You'll get term suggestion, spell check, porting, stemming and much more. It's really very nice.
See here for a full listing of its features relating to queries.
There are Django and PHP libraries for Solr.
I wouldn't recommend using Google Suggest for such a specialised corpus anyway, and with Solr you won't need it.
Hope this helps.
Is there a research paper/book that I can read which can tell me for the problem at hand what sort of feature selection algorithm would work best.
I am trying to simply identify twitter messages as pos/neg (to begin with). I started out with Frequency based feature selection (having started with NLTK book) but soon realised that for a similar problem various individuals have choosen different algorithms
Although I can try Frequency based, mutual information, information gain and various other algorithms the list seems endless.. and was wondering if there an efficient way then trial and error.
any advice
Have you tried the book I recommended upon your last question? It's freely available online and entirely about the task you are dealing with: Sentiment Analysis and Opinion Mining by Pang and Lee. Chapter 4 ("Extraction and Classification") is just what you need!
I did an NLP course last term, and it came pretty clear that sentiment analysis is something that nobody really knows how to do well (yet). Doing this with unsupervised learning is of course even harder.
There's quite a lot of research going on regarding this, some of it commercial and thus not open to the public. I can't point you to any research papers but the book we used for the course was this (google books preview). That said, the book covers a lot of material and might not be the quickest way to find a solution to this particular problem.
The only other thing I can point you towards is to try googling around, maybe in scholar.google.com for "sentiment analysis" or "opinion mining".
Have a look at the NLTK movie_reviews corpus. The reviews are already pos/neg categorized and might help you with training your classifier. Although the language you find in Twitter is probably very different from those.
As a last note, please post any successes (or failures for that matter) here. This issue will come up later for sure at some point.
Unfortunately, there is no silver bullet for anything when dealing with machine learning. It's usually referred to as the "No Free Lunch" theorem. Basically a number of algorithms work for a problem, and some do better on some problems and worse on others. Over all, they all perform about the same. The same feature set may cause one algorithm to perform better and another to perform worse for a given data set. For a different data set, the situation could be completely reversed.
Usually what I do is pick a few feature selection algorithms that have worked for others on similar tasks and then start with those. If the performance I get using my favorite classifiers is acceptable, scrounging for another half percentage point probably isn't worth my time. But if it's not acceptable, then it's time to re-evaluate my approach, or to look for more feature selection methods.
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Closed 10 years ago.
I remember the old effective approach of studying a new framework. It was always the best way to read a good book on the subject, say MFC. When I tried to skip a lot of material to speed up coding it turned out later that it would be quicker to read the whole book first. There was no good ways to study a framework in small parts. Or at least I did not see them then.
The last years a lot of new things happened: improved search results from Google, programming blogs, much more people involved in Internet discussions, a lot of open source frameworks.
Right now when we write software we much often depend on third-party (usually open source) frameworks/ libraries. And a lot of times we need to know only a small amount of their functionality to use them. It's just about finding the simplest way of using a small subset of the library without unnecessary pessimizations.
What do you do to study as less as possible of the framework and still use it effectively?
For example, suppose you need to index a set of documents with Lucene. And you need to highlight search snippets. You don't care about stemmers, storing the index in one file vs. multiple files, fuzzy queries and a lot of other stuff that is going to occupy your brain if you study Lucene in depth.
So what are your strategies, approaches, tricks to save your time?
I will enumerate what I would do, though I feel that my process can be improved.
Search "lucene tutorial", "lucene highlight example" and so on. Try to estimate trust score of unofficial articles ( blog posts ) based on publishing date, the number and the tone of the comments. If there is no a definite answer - collect new search keywords and links on the target.
Search for really quick tutorials/ newbie guides on official site
Estimate how valuable are javadocs for a newbie. (Read Lucene highlight package summary)
Search for simple examples that come with a library, related to what you need. ( Study "src/demo/org/apache/lucene/demo")
Ask about "simple Lucene search highlighting example" in Lucene mail list. You can get no answer or even get a bad reputation if you ask a silly question. And often you don't know whether you question is silly because you have not studied the framework in depth.
Ask it on Stackoverflow or other QA service "could you give me a working example of search keywords highlighting in Lucene". However this question is very specific and can gain no answers or a bad score.
Estimate how easy to get the answer from the framework code if it's open sourced.
What are your study/ search routes? Write them in priority order if possible.
I use a three phase technique for evaluating APIs.
1) Discovery - In this phase I search StackOverflow, CodeProject, Google and Newsgroups with as many different combination of search phrases as possible and add everything that might fit my needs into a huge list.
2) Filter/Sort - For each item I found in my gathering phase I try to find out if it suits my needs. To do this I jump right into the API documentation and make sure it has all of the features I need. The results of this go into a weighted list with the best solutions at the top and all of the cruft filtered out.
3) Prototype - I take the top few contenders and try to do a small implementation hitting all of the important features. Whatever fits the project best here wins. If for some reason an issue comes up with the best choice during implementation, it's possible to fall back on other implementations.
Of course, a huge number of factors go into choosing the best API for the project. Some important ones:
How much will this increase the size of my distribution?
How well does the API fit with the style of my existing code?
Does it have high quality/any documentation?
Is it used by a lot of people?
How active is the community?
How active is the development team?
How responsive is the development team to bug patch requests?
Will the development team accept my patches?
Can I extend it to fit my needs?
How expensive will it be to implement overall?
... And of course many more. It's all very project dependent.
As to saving time, I would say trying to save too much here will just come back to bite you later. The time put into selecting a good library is at least as important as the time spent implementing it. Also, think down the road, in six months would you rather be happily coding or would you rather be arguing with a xenophobic dev team :). Spending a couple of extra days now doing a thorough evaluation of your choices can save a lot of pain later.
The answer to your question depends on where you fall on the continuum of generality/specificity. Do you want to solve an immediate problem? Are you looking to develop a deep understanding of the library? Chances are you’re somewhere between those extremes. Jeff Atwood has a post about how programmers move between these levels, based on their need.
When first getting started, read something on the high-level design of the framework or library (or language, or whatever technology it is), preferably by one of the designers. Try to determine what problems they are trying to address, what the organizing principles behind the design are, and what the central features are. This will form the conceptual framework from which future understanding will hang.
Now jump in to it. Create something. Do not copy and paste somebody's code. Instead, when things don’t work, read the error messages in detail, and the help on those error messages, and figure out why that error occurred. It can be frustrating, when things don’t work, but it forces you to think, and that’s when you learn.
1) Search Google for my task
2) look at examples with a few different libraries, no need to tie myself down to Lucene for example, if I don't know what other options I have.
3) Look at the date of last update on the main page, if it hasn't been updated in 6-months leave (with some exceptions)
4) Search for sample task with library (don't read tutorials yet)
5) Can I understand what's going on without a tutorial? If yes continue if no start back at 1
6) Try to implement the task
7) Watch myself fail
8) Read a tutorial
9) Try to implement the task
10) Watch myself fail and ask on StackOverflow, or mail the authors, post on user group (if friendly looking)
11) If I could get the task done, I'll consider the framework worthy of study and read up the main tutorial for 2 hours (if it doesn't fit in 2 hours I just ignore what's left until I need it)
I have no recipe, in the sense of a set of steps I always follow, that's largely because everything I learn is different. Some things are radically new to me (Dojo for example, I have no fluency in Java script so that's a big task), some just enhancements of previous knowledge (Iknow EJB 2 well, so learning EJB 3 while on the surface is new with all its annotations, its building on concepts.)
My general strategy though is I'd describe as "Spiral and Park". I try to circle the landscape first, understand the general shape, I Park concepts that I don't get just yet, don't let it worry me. Then i go a little deeper into some areas, but again try not to get obsessed with one, Spiralling down into the subject. Hopefully I start to unpark and understand, but also need to park more things.
Initially I want answers to questions such as:
What's it for?
Why would I use this rather than that other thing I already know
What's possible? Any interesting sweet spots. "Eg. ooh look at that nice AJAX-driven update"
I do a great deal of skim reading.
Then I want to do more exploring on the hows. I start to look for gotchas and good advice. (Eg. in java: why is "wibble".equals(var) a useful construct?)
Specific techniques and information sources:
Most important: doing! As early as possible I want to work a tutorial or two. I probably have to get the first circuit of the spiral done, but then I want to touch and experiment.
Overview documents
Product documents
Forums and discussion groups, learning by answering questions is my favourite technique.
if at all possible I try to find gurus. I'm fortunate in having in my immediate colleagues a wealth of knowledge and experience.
Quick-start guides.
A quick look at the API documentation if available.
Reading sample codes.
Messing around YOU HAVE TO MESS AROUND (sorry for the caps).
If it's a small library/API with a small or no community you can always contact the developer himself and ask for help 'cause he'll probably be more than happy to help you; he's happy that one more person is using his API.
Mailing lists are a great resource as long as you do your homework first before asking questions.
Mailing list archives are invaluable for most of the questions I've had on CoreAudio related stuff.
I would never read javadoc. As there often is none. And when there is, most likely it isnt up to date. So one gets confused at the best.
Start with the simplest possible tutorial you find within some minutes.
Often the tutorial will lead you to further sources at the end, so then most of the time one is on a path that goes on and on, deeper and deeper.
It really depends on what the topic is and how much info is on it. Learning by example is a good way to start a topic brand new to you, especially if you're knowledgeable in other similar libraries or languages. You can take a topic you're familiar with, and say "I understand how to implement using X, lets see how it's done using Y".
So what are your strategies, approaches, tricks to save your time?
Well, I search. I generally never ask questions, preferring to research myself. If worse comes to worse I'll read the documentation. In some cases (say, when I was doing some work with SharpSVN) I had to look at the source, specifically the test cases, to get some information about how the API worked.
Generally, I have to be honest, most of my 'study' and 'learning' is by accident.
For example, just a few seconds ago, I discovered how to get the "Recent" folder in C#. I had no idea how to do that before seeing the question, considering it interesting, and then searching.
So for me the real 'trick' is that I hang around on forums, answer questions, and accidentally pick up knowledge. Then when it comes time for me to research something; chances are I know a bit about it, and searching is easier and I can focus on the implementation [typically implementing a test program first] and progressing from there.
I think there is a wealth of natural language data associated with sites like reddit or digg or news.google.com.
I have done a little bit of research with text mining, but can't find how I could use those tools to parse something like reddit.
What kind of applications can you come up with?
I have found in the past that the best way to mine data on sites like Reddit or Digg is to first use the developer API that they provide. Typically you have a focused interest in either a topic or trend, and the only way to get that data is through an established public interface. You can also parse feeds, and combine them both to uncover 90% of what you would want to know. If you want to do deep research on data not available through an API, then you should be prepared to spend a significant amount of time writing custom wrappers around a tool like cURL. If you have the budget you can also call them and ask if they offer paid research data on users.
I'd start on the RSS, and after that I might use Nutch; what to actually do with the data is more your call.
These are good ideas. I can get the data, but what applications can be built around it?
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Closed 11 years ago.
I've always been interested in developing a web search engine. What's a good place to start? I've heard of Lucene, but I'm not a big Java guy. Any other good resources or open source projects?
I understand it's a huge under-taking, but that's part of the appeal. I'm not looking to create the next Google, just something I can use to search a sub-set of sites that I might be interested in.
There are several parts to a search engine. Broadly speaking, in a hopelessly general manner (folks, feel free to edit if you feel you can add better descriptions, links, etc):
The crawler. This is the part that goes through the web, grabs the pages, and stores information about them into some central data store. In addition to the text itself, you will want things like the time you accessed it, etc. The crawler needs to be smart enough to know how often to hit certain domains, to obey the robots.txt convention, etc.
The parser. This reads the data fetched by the crawler, parses it, saves whatever metadata it needs to, throws away junk, and possibly makes suggestions to the crawler on what to fetch next time around.
The indexer. Reads the stuff the parser parsed, and creates inverted indexes into the terms found on the webpages. It can be as smart as you want it to be -- apply NLP techniques to make indexes of concepts, cross-link things, throw in synonyms, etc.
The ranking engine. Given a few thousand URLs matching "apple", how do you decide which result is the best? Jut the index doesn't give you that information. You need to analyze the text, the linking structure, and whatever other pieces you want to look at, and create some scores. This may be done completely on the fly (that's really hard), or based on some pre-computed notions of "experts" (see PageRank, etc).
The front end. Something needs to receive user queries, hit the central engine, and respond; this something needs to be smart about caching results, possibly mixing in results from other sources, etc. It has its own set of problems.
My advice -- choose which of these interests you the most, download Lucene or Xapian or any other open source project out there, pull out the bit that does one of the above tasks, and try to replace it. Hopefully, with something better :-).
Some links that may prove useful:
"Agile web-crawler", a paper from Estonia (in English)
Sphinx Search engine, an indexing and search api. Designed for large DBs, but modular and open-ended.
"Information Retrieval, a textbook about IR from Manning et al. Good overview of how the indexes are built, various issues that come up, as well as some discussion of crawling, etc. Free online version (for now)!
Xapian is another option for you. I've heard it scales better than some implementations of Lucene.
Check out nutch, it's written by the same guy that created Lucene (Doug Cutting).
It seems to me that the biggest part is the indexing of sites. Making bots to scour the internet and parse their contents.
A friend and I were talking about how amazing Google and other search engines have to be under the hood. Millions of results in under half a second? Crazy. I think that they might have preset search results for commonly searched items.
edit:
This site looks rather interesting.
I would start with an existing project, such as the open source search engine from Wikia.
[My understanding is that the Wikia Search project has ended. However I think getting involved with an existing open-source project is a good way to ease into an undertaking of this size.]
http://re.search.wikia.com/about/get_involved.html
If you're interested in learning about the theory behind information retrieval and some of the technical details behind implementing search engines, I can recommend the book Managing Gigabytes by Ian Witten, Alistair Moffat and Tim C. Bell. (Disclosure: Alistair Moffat was my university supervisor.) Although it's a bit dated now (the first edition came out in 1994 and the second in 1999 -- what's so hard about managing gigabytes now?), the underlying theory is still sound and it's a great introduction to both indexing and the use of compression in indexing and retrieval systems.
I'm interested in Search Engine too. I recommended both Apache Hadoop MapReduce and Apache Lucene. Getting faster by Hadoop Cluster is the best way.
There are ports of Lucene. Zend have one freely available. Have a look at this quick tutorial: http://devzone.zend.com/node/view/id/91
Here's a slightly different approach, if you are not so much interested in the programming of it but more interested in the results: consider building it using Google Custom Search Engine API.
Advantages:
Google does all the heavy lifting for you
Familiar UI and behavior for your users
Can have something up and running in minutes
Lots of customization capabilities
Disadvantages:
You're not writing code, so no learning opportunity there
Everything you want to search must be public & in the Google index already
Your result is tied to Google