I am working on a graduation project related to "Aspect extraction (AE)".
I'm pretty confused about POS taging, syntax tree, grammar rules, and other low-level NLP stuff. I need a reference that teaches me these things in detail, so if any of you know I hope you don't mind me?
I know my question is not about programming directly and this may not agree with the site, but I really need to.
This is not an easy one! I assume that you are trying to understand the underlying 'why' and 'what', so, if I were you I would start with the one and only "Speech and Language Processing" by Daniel Jurafsky and James H. Martin. They have a whole section (Section 17 in my Second edition) on the representation of meaning, and state representation, with a whole subsection on Aspect.
In addition to that, the book will also help you understand various existing approaches to POS-tagging and the other topics you mentioned above, and, the book is available online for free! There is even the draft of the 3rd edition out there.
Additionally, after reading the chapters above, you can check out how other people do aspect extraction here
I am working in NLP Project and I am looking for parser to construct simple Sentences from complex one, written in C# . Since Sentences may have complex grammatical structure with multiple embedded clauses.
Any Help ?
Text summarisation and sentence simplification are very much an open research area. Wikipedia has articles about both, you can start from there. Beware: this is hard problem and chances are the state of the art system is far worse than you might expect. There isn't an off-the-shelf piece of software that you can just grab and solve all your problems. You will have some success with the more basic sentences, but performance will degrade as you complex sentence gets more complex. Have a look at the articles referenced on Wikipedia or google around to get an idea of what is possible. My impression is most readily available software packages are for academic purposes and might take a bit of work to get running.
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.
The title may seem slightly self-contradictory, and I accept that you can't really learn a language quickly. However, an experienced programmer that already has knowledge of a few languagues and different styles (functional, OO, imperative etc.) often wants to get started quickly. I've seen a few websites doing effective "translations" in the form of "just show me syntax equivalence". I can't remember the sites now, but for related languages (e.g. Perl/PHP) it's quite common.
Is there a better resource that covers more languages? Is there a resource that covers idioms as well as syntax? I think this would be incredibly useful for doing small amounts of work on existing code bases where you are not familiar with the language. Looking at the existing code, as we know, is not always a good indicator of quality. Likewise, for "learn by doing" weekend project I always have the urge to write reasonably idiomatic, clean code from the start. Such a resource could also link to known good example projects of varying sizes for those that prefer to learn by reading. Reading a well-written medium sized code base can also be much more practical when access to development environments might be limited.
I think it's possible to find tutorials and summaries for individual languages that provide some of this functionality in disparate web locations but I'm hoping there is a good, centralised, comparative place that the busy programmer can turn to.
You generally have two main things to overcome:
Syntax
Reference
Syntax you can pick up fairly quickly with a language tutorial and a stack of samplecode.
Reference (library/API calls) you need to find a proper guide to; perhaps the language reference, or perhaps google...
With those two in place, following a walkthrough (to get you used to using the development environment) will have you pretty much ready - you'll be able to look up what you want to say (reference), and know how to say it (syntax).
This, of course, applies principally to procedural/oop languages; languages that require a paradigm switch (ML/Haskell) you should go to lectures for ;)
(and for the weirder moments, there's SO!)
In the past my favour was "learning by doing". So e.g. I know a little bit of C++ and a lot of C#.Net but I must write a FTP Tool in Python.
So I sit for an hour and so the syntax differences by a tutorial, than I develop the form itself and look at the generated code. Then I search a open source Python FTP Client and get pieces of code (Not copy and paste, write it self to see, feel and remember the code!)
After a few hours I get it.
So: The mix is the best. A book, a piece of good code, the willing to learn and a free night with much coffee.
At the risk of sounding cheesy, I would start with the language's website tutorial and/or FAQ, followed by asking more specific questions here. SO is my centralized location for programming knowledge.
I remember when I learned Perl. I was asked to modify some Perl code at work and I'd never seen the language before. I had experience with several other languages, however, so it wasn't hard to figure out the syntax with the online Perl docs in one window and the code in another, side-by-side. I don't know that solely reading existing code is necessarily the best way to learn. In my case, I didn't know Perl but I could tell that the person who originally wrote the code didn't know Perl either. I'm not sure I could've distinguished between good Perl and really confusing Perl. It would've been nice to be able to ask questions here at the time.
Language isn't important. What is important is learning your ways around designing algorithms and the proper application of design patterns. Focus on the technique, not the language that implements a certain technique. Once you understand the proper development techniques, any programming language will just become real easy, no matter how obscure they are...
When you put a focus on a language, you're restricting your own knowledge.
http://devcheatsheet.com/ seems to be a step in the right direction: it aggregates cheat sheets/quick references and they are (somewhat) manually reviewed. It's also wide-ranging. It still comes up short a bit in terms of "idiomatic" quick reference: for example, the page on Ruby doesn't mention yield.
Rosetta Code appears to be an excellent resource that includes hints on coding idiomatically and moves from simple (like for-loops) to things like drawing. I haven't checked out how comprehensive it is, but there are a large number of languages and tasks listed. The drawbacks re: original question are:
Some of the linking is not accurate
(navigating Python->ForLoop will
take you to the top of the ForLoop
page, not the Python section). It's a
wiki, this can be improved.
Ideally you could "slice" the wiki
however you chose to see e.g. the top
20 tasks for two languages
side-by-side.
http://hyperpolyglot.org/ seems to be an almost perfect match for what I was looking for. The quality is not always there, or idiom can be lacking, but it has the same intention and is pretty comprehensive.
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Question
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.