I've come to understand that suffix trees are excellent and useful structures for a multitude of string related tasks, and I would like to learn more about them. Can anyone suggest a good starting point for UNDERSTANDING these things? That is, I don't need some ready made code or library that implements it, but maybe some tutorials that show how they are built, and what you can do with them. I enjoy "recreational programming", and suffix trees are high on my list of things to learn :)
PS: I prefer Delphi/pascal, but tutorials in any language are welcome.
Wikipedia is a great place to start. The Suffix Tree article has plenty of references and external links. The NIST page is kind of light. There is also an article from Dr. Dobb's Journal. In general, I'd recommend the Cormen / Leiserson / Rivest / Stein Algorithms book, but I don't have a copy handy so can't confirm that they actually cover Suffix Trees.
I am looking for some resources pertaining to the parsing and understanding of English (or just human language in general). While this is obviously a fairly complicated and wide field of study, I was wondering if anyone had any book or internet recommendations for study of the subject. I am aware of the basics, such as searching for copulas to draw word relationships, but anything you guys recommend I will be sure to thoroughly read.
Thanks.
Check out WordNet.
You probably want a book like "Representation and Inference for Natural Language - A First Course in Computational Semantics"
http://homepages.inf.ed.ac.uk/jbos/comsem/book1.html
Another way is looking at existing tools that already do the job on the basis of research papers: http://nlp.stanford.edu/index.shtml
I've used this tool once, and it's very nice. There's even an online version that lets you parse English and draws dependency trees and so on.
So you can start taking a look at their papers or the code itself.
Anyway take in consideration that in any field, what you get from such generic tools is almost always not what you want. In the sense that the semantics attributed by such tools is not what you would expect. For most cases, given a specific constrained domain it's preferable to roll your own parser, and do your best to avoid any ambiguities beforehand.
The process that you describe is called natural language understanding. There are various algorithms and software tools that have been developed for this purpose.
I have no programming experience but am interested in learning a language.
So reading this section "http://wiki.freaks-unidos.net/weblogs/azul/principles-of-software#extend-your-language-to-match-your-domain" made me curious about programming a single application in 2 or more languages.
How is it actually done?
A few thoughts:
The page you linked to explains pretty clearly how it's done
If you are interested in learning a language, this is probably not the place to start
Programing a single application in two or more languages is only marginally related to the linked document.
Still, in the face of all that, I'll try to give an example of how this works by analogy.
Suppose you need to work with a group of people on some technical task--ranking chess puzzles by difficulty or testing marshmallows for contamination or something. Suppose further that one of the people on your team speaks only Japanese, another only Portuguese, and the third only Esperanto.
Being blessed with the ability to speak all of these languages fluently, your best bet is to make up an artificial language specialized to the task at hand; this is called a Domain Specific Language, or DSL. It should have all the terminology you need to talk about knights and rooks or silicate nanoparticles or whatever for the task, and not much else. Teach this to each of your team members, and then you can give them all their instructions at the same time. They can talk to each other about what they are doing, ask for help (so long as it's related to something covered by your language) as if they all spoke the same language.
That's roughly what he's talking about.
I think you may be trying to run before you can walk. The concepts in there probably require a little programming experience to start with.
The thrust of the article (and frankly poorly expressed) is that when you are programming you often encounter tasks that benefit from a declarative syntax, i.e. you should be able to express the intent of what you want to do and leave the implementation details to a library. A good example is querying a database, it's much more readable (usually) to be able to declaratively describe what you want to do and let some middleware figure out the best way to do it, SQL and Linq are 2 examples of a declarative mechanism for querying data.
This is a very interesting topic, but honestly if you have no programming experience it's probably more of a 201 subject than a 101 subject, get your basics down first.
I need do write an expert systems that should aid user in picking up best mobile phone operator. It should be very simple and not based on languages/libaries such as CLISP or JESS. So I need to write it all from the ground up.
Do you know some books or online tutorials that explains how this can be done?
What I really need to get to know is how to represent knowledge and facts.
Any help would be much appreciated.
If you get any of the good texts on AI, there will be a section on expert systems; you can, if forced, work it out from there and implement your own.
The basic idea is really fairly simple: you have a collection of rules in "if-then" form that represent inferences, or4 implications. Like, for example:
IF blood temperature > 41°C
THEN patient.has-fever := TRUE
IF patient has wet-sounding breathing
THEN patient.has-pneumonia
IF patient.has-fever AND patient.has-pneumonia
THEN CONCLUDE bacterial pneumonia. ACTION prescribe Augmentin
In other words, you have a bunch of rules, and you evaluate the rules until you get to a conclusion. There's a lot more to is (forward or backward chaing and that kind of thing) which you can read about in thed pretty decent Wikipedia article.
I'm puzzled why you can't use an existing rule engine though -- there are a number of them, for most languages, usually under pretty liberal licenses. That's really an easier route unless this is a homework problem or something.
Prolog is well suited to writing rule-based systems (a pretty standard approach to expert systems development). P# compiles to C#, which may meet your needs - and it's free.
More information on P#.
The basis rationale, and mathematical proof, for the PROLOG language, should help you understand most of the concepts you will need to address, if not provide the final language you need to use to implement it.
I couldn't find a link to the original implementation, but it would not help you much anyway. Alain Colmerauer's early work on logic programming should be helpfull.
[EDIT] Sorry, duplicate...
I would vote for some implementation of Prolog or CLIPS, depending if backward or forward chaining logic best suits the problem. Instead of re-implementing either of these, spend the time working out how to integrate them with your environment.
Jess is a good choice but you should read the book "Jess in action" as a first step.
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So I've recently came up with some new possible projects that would have to deal with deriving 'meaning' from text submitted and generated by users.
Natural language processing is the field that deals with these kinds of issues, and after some initial research I found the OpenNLP Hub and university collaborations like the attempto project. And stackoverflow has this.
If anyone could link me to some good resources, from reseach papers and introductionary texts to apis, I'd be happier than a 6 year-old kid opening his christmas presents!
Update
Through one of your recommendations I've found opencyc ('the world's largest and most complete general knowledge base and commonsense reasoning engine'). Even more amazing still, there's a project that is a distilled version of opencyc called UMBEL. It features semantic data in rdf/owl/skos n3 syntax.
I've also stumbled upon antlr, a parser generator for 'constructing recognizers, interpreters, compilers, and translators from grammatical descriptions'.
And there's a question on here by me, that lists tons of free and open data.
Thanks stackoverflow community!
Tough call, NLP is a much wider field than most people think it is. Basically, language can be split up into several categories, which will require you to learn totally different things.
Before I start, let me tell you that I doubt you'll have any notable success (as a professional, at least) without having a degree in some (closely related) field. There is a lot of theory involved, most of it is dry stuff and hard to learn. You'll need a lot of endurance and most of all: time.
If you're interested in the meaning of text, well, that's the Next Big Thing. Semantic search engines are predicted as initiating Web 3.0, but we're far from 'there' yet. Extracting logic from a text is dependant on several steps:
Tokenization, Chunking
Disambiguation on a lexical level (Time flies like an arrow, but fruit flies like a banana.)
Syntactic Parsing
Morphological analysis (tense, aspect, case, number, whatnot)
A small list, off the top of my head. There's more :-), and many more details to each point. For example, when I say "parsing", what is this? There are many different parsing algorithms, and there are just as many parsing formalisms. Among the most powerful are Tree-adjoining grammar and Head-driven phrase structure grammar. But both of them are hardly used in the field (for now). Usually, you'll be dealing with some half-baked generative approach, and will have to conduct morphological analysis yourself.
Going from there to semantics is a big step. A Syntax/Semantics interface is dependant both, on the syntactic and semantic framework employed, and there is no single working solution yet. On the semantic side, there's classic generative semantics, then there is Discourse Representation Theory, dynamic semantics, and many more. Even the logical formalism everything is based on is still not well-defined. Some say one should use first-order logic, but that hardly seems sufficient; then there is intensional logic, as used by Montague, but that seems overly complex, and computationally unfeasible. There also is dynamic logic (Groenendijk and Stokhof have pioneered this stuff. Great stuff!) and very recently, this summer actually, Jeroen Groenendijk presented a new formalism, Inquisitive Semantics, also very interesting.
If you want to get started on a very simple level, read Blackburn and Bos (2005), it's great stuff, and the de-facto introduction to Computational Semantics! I recently extended their system to cover the partition-theory of questions (question answering is a beast!), as proposed by Groenendijk and Stokhof (1982), but unfortunately, the theory has a complexity of O(n²) over the domain of individuals. While doing so, I found B&B's implementation to be a bit, erhm… hackish, at places. Still, it is going to really, really help you dive into computational semantics, and it is still a very impressive showcase of what can be done. Also, they deserve extra cool-points for implementing a grammar that is settled in Pulp Fiction (the movie).
And while I'm at it, pick up Prolog. A lot of research in computational semantics is based on Prolog. Learn Prolog Now! is a good intro. I can also recommend "The Art of Prolog" and Covington's "Prolog Programming in Depth" and "Natural Language Processing for Prolog Programmers", the former of which is available for free online.
Chomsky is totally the wrong source to look to for NLP (and he'd say as much himself, emphatically)--see: "Statistical Methods and Linguistics" by Abney.
Jurafsky and Martin, mentioned above, is a standard reference, but I myself prefer Manning and Schütze. If you're serious about NLP you'll probably want to read both. There are videos of one of Manning's courses available online.
If you get through Prolog until the DCG chapter in Learn Prolog Now! mentioned by Mr. Dimitrov above, you'll have a good beginning at getting some semantics into your system, since Prolog gives you a very simple way of maintaining a database of knowledge and belief, which can be updated through question-answering.
As regards the literature, I have one major recommendation for you: run out and buy Speech and Language Processing by Jurafsky & Martin. It is pretty much the book on NLP (the first chapter is available online); used in a frillion university courses but also very readable for the non-linguist and practically oriented, while at the same time going fairly deep into the linguistics problems. I really cannot recommend it enough. Chapters 17, 18 and 21 seem to be what you're looking for (14, 15 and 18 in the first edition); they show you simple lambda notation which translates pretty well to Prolog DCG's with features.
Oh, btw, on getting the masters in linguistics; if NL semantics is what you're into, I'd rather recommend taking all the AI-related courses you can find (although any courses on "plain" linguistic semantics, logic, logical semantics, DRT, LFG/HPSG/CCG, NL parsing, formal linguistic theory, etc. wouldn't hurt...)
Reading Chomsky's original literature is not really useful; as far as I know there are no current implementations that directly correspond to his theories, all the useful stuff of his is pretty much subsumed by other theories (and anyone who stays near linguists for any matter of time will absorb knowledge of Chomsky by osmosis).
I'd highly recommend playing around with the NLTK and reading the NLTK Book. The NLTK is very powerful and easy to get into.
You could try reading up a bit on phrase structured grammers, which is basically the mathematics behind much language processessing. It's actually not that heavy, being largely based on set and graph theory. I studied it many moons ago as part of a discrete math course, and I guess there are many good references available at this stage.
Edit:Not as much as I expected on google, although this one looks like a good learning source.
One of the early explorers into NLP is Noam Chomsky; he wrote small books on the subject in the 50s through the 70s. You may find that engaging reading.
Cycorp have a short description of how their Cyc knowledge base derives meaning from sentences.
By utilising a massive knowledge base of common facts, the system can determine the most logical parse of a sentence.
A simpler place to begin with the building blocks is the look at the documentation for a package that attempts to do it. I'd recommend the Python [Natural Language Toolkit (NLTK)1, particularly because of their well-written, free book, which is filled with examples. It won't get you all the way to what you want (which is an AI-hard problem), but it will give you a good footing. NLTK has parsers, chunkers, context-free grammars, and more.
This is really hard stuff. I'd start off by getting at least a Masters in Linguistics, and then work towards my PhD in computer science, concentrating on NLP.
The problem is that most of us don't have the understanding of what language is. And without that understanding, it's bloody tough to implement a solution.
Other comments give some readings, which are probably fine if you want to get started playing around with a small subset of the problem, but in order to come up with a really robust solution, then there are no shortcuts. You need the academic background in both disciplines.
A very enjoyable readable introduction is The Language Instinct by Steven Pinker. It goes into the Chomsky stuff and also tells interesting stories from the evolutionary biology angle. Might be worth starting with something like that before diving into Chomsky's papers and related work, if you're new to the subject.