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Creating a Computational Linguistics College Degree? 47

Posted by Cliff from the make-your-own-major dept.
$random_var asks: "I am an undergraduate student currently studying Bioengineering. However, I am growing more and more interested in programming and linguistics, which leads me to think that I should define my own major, Computational Linguistics [mit.edu], which google defines as 'a field concerned with the processing of natural language by computers.' When I present my proposal for this degree to the school's advising staff, I would like to have a complete list of all of the topics this major should cover. Having only little experience with computer science and engineering, I'm not sure what parts of that field I should include. Beyond the basic lower-division courses, what specific fields of computer science do you think should be emphasized in a practical undergraduate study of Computational Linguistics?"
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Creating a Computational Linguistics College Degree? More

Creating a Computational Linguistics College Degree?

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  • As someone who has done some work in the field... (Score:5, Interesting)

    by Faizdog ( 243703 ) on Monday November 07, 2005 @07:03PM (#13974458)
    Computational Linguistics, or Natural Language Processing (NLP) as it was called for me, is one of the many areas that traditional Computer Science is exploring, in addition to things like biology (bioinformatics), etc.

    I'd say the first two years in the major need to be very similar to the first two years in the Computer Science curriculum and last two in the humanities/linguistic area. The reason I say this is because a lot of the math, basic computer science, etc that is needed in the field will be the same as the core Comp Sci/engineering classes. However, the first two years in the humanities are very generic, and you specialize a lot later. So build up the core of Comp Sci and linguistical study early.

    Then, get deeper. A lot of the NLP work is being done in Maching Learning/Data Mining classes. Make sure you take those, we had a whole class called Textual Data Mining at my university. Take algorithm design and some of the common advanced Comp Sci classes too, since a lot of the techniques are very advanced, being developed, cutting edge and will require research. Take advanced statistics classes too, much of the field is built on statistics.

    NLP is a very interesting area, but I don't know if it deserves its own major yet. I would advise majoring in comp sci, with a concentration in Machine Learning/Data Mining through your technical electives. And a minor, or perhaps second major in Linguistics. I'd say minor because then you could take only the classes relevant to the field, instead of all the other stuff related to a humanities major that you may not want.

    Anyway, that's my take. I did a bunch of NLP research, even getting work published as an undergrad, and I was a Computer Engineering major. The field is as such that it's so new and emerging that not much formal linguistics study is required right now, if you are a native english speaker you are probably good. But, it doesn't hurt to get a more formal background in it, that's why I suggested the minor.

    • NLP is a very interesting area, but I don't know if it deserves its own major yet. I would advise majoring in comp sci, with a concentration in Machine Learning/Data Mining through your technical electives.

      That's the first thing I thought when reading this question. This seems way too concentrated for a bachelor's degree. I'd say either doing computer science or computer engineering would be the way to go, followed by grad school. Find the right school with the right research group and/or professor and

    • I worked in the field for quite a while, with a formal background in physics and a personal background in linguistics, and I agree with the parent. Major in CS, emphasizing the mathematical side over the technological side (e.g., algorithms vs. memorizing C++ trivia). Minor in linguistics (not "humanities", but real linguistics, which can be quite hard core.) Don't bother trying to officially create a "major". If you do what I (and the parent) suggest, you can always claim to have gotten your B.S. in comput

    • Missing (Score:1)

      by Tune ( 17738 )
      I have similar education in CS and CL, worked in CL and AI/OR. IMHO, and as mentioned in others posts you're missing the more formal subjects. Scanning, parsing, translation, logic specialized to formal semantics, syntax and discourse should be in any basic Computational Linguistics (masters) course as well.

  • A.I. (Score:2, Interesting)

    by cmarks03 ( 900042 )
    The only thing as a CSE student that pops out at me is Artificial Intelligence-related courses. Though if you ask me, the best way learn how to do something is by doing it (assuming you have taken the entry-level courses like C, etc). I'm doing an internship (co-op) right now that will help me in at least three classes I have to take still.
    Also, just thinking and re-reading, some language classes would be handy. Though the elementary school english about nouns/verbs/etc will help, you'll need more if i
    • AI doesn't have much to do with NLP, actually. You'd employ some sort of semantic parser after the phonetic analysis, phonemic analysis, and morphosyntactic analysis, depending on your needs. That parser would likely employ some probabilistic AI techniques to deal with ambiguity via context--depending on the quality of the parser.

      On the other hand, the biggest problems in NLP are building large machine-readable corpuses (lots of grunt work) and interpreting the parsed results. That could involve AI techniqu

  • Copy the syllabus from (Score:1, Informative)

    by Anonymous Coward
    http://www.cstit.cl.cam.ac.uk/lms/pages/current/ho mepages/index.html [cam.ac.uk].

    Computer Speech, Text and Internet Technology has to be the lamest course name in history but the course itself is very good with both computation and linguistics aspects, as you would expect from Cambridge...

  • Don't do it (Score:5, Insightful)

    by AuMatar ( 183847 ) on Monday November 07, 2005 @07:30PM (#13974697)
    Not as an undergrad. You're still learning, still ought to be figurin g out what you want to do. Get a good, well rounded CS education. Maybe take a few courses in linguistics if the subject interests you. Then specialize in computational linguistics in grad school if you decide its really what you want to do.

    Basicly, undergrad is to learn the field. A masters is to specialize in one domain. A doctorate is to research a single problem in as much depth as possible. They're in that order for good reason- it leaves you plenty of room to change your mind and interest as you grow older, while keeping you marketable in both industry and academia. Don't specialize too early, or you may regret it later.

    • Re:Don't do it (Score:3, Interesting)

      by octavist ( 881608 )
      I concur. In fact, I did what you are trying to do, complete with Engineering Open House project that conversed, and stored info in a semantic network. Then I had wonderful interviews with various NLP heavyweights (of the era). When one gentleman said, "I'm going to do everything I can to see you get an offer", I realize in retrospect that he meant, "...since you only have a Bachelor's Degree." CS, lots of graph theory and algorithms. Take a few linguistics courses on the side. What he said. Then do
    • Do not do it. Bioengineering is a way better degree, in fact, ChemE is even better.. get a CS minor or double major. Study linear algebra and dynamical systems. You can always do computational linguistics in grad school (and it is dying field anyway so far)... Anyway, how do you think you compute all that stuff anyway? Matrices, Markov chains, and other mathemtical algorithms. GET AS MUCH Math
      training as you possibily can. Linear Algebra is essential.

    • Basicly, undergrad is to learn the field. A masters is to specialize in one domain. A doctorate is to research a single problem in as much depth as possible.

      When I went back to school a few years ago for my Masters, I described the levels of degrees like this:

      Undergrad: Do XYZ.

      Masters: Find out something interesting about XYZ.

      Ph.D.: Based on XYZ, invent ABC.

      Yes, I found out interesting things.

      ...laura, B.Sc, M.A.Sc.

  • Is a separate major really necessary? (Score:4, Insightful)

    by dslauson ( 914147 ) on Monday November 07, 2005 @07:40PM (#13974784) Journal
    To me, this sounds like a good idea for your specialization for a masters degree (in computer science), but I don't really think it justifies it's own undergraduate degree. If that's what you're really interested in, I recommend majoring in CS as an undergrad, perhaps with a minor in english, and then submitting this your linguistics stuff as your masters thesis when you get there.

    Of course, there are a lot of classes to steer you in that direction. From the computer science field, you have to take compiler structure. That may not sound like a relevant class, but it is. Learning to write good lexers and parsers is going to be vital, and that is the class where you will learn them. Also, seeing how a high-level programming language is parsed can give you a lot of insight into how natural language will be parsed.

    Of course, an AI class would be helpful, too. And machine learning, but that's not always offered at the undergraduate level.

    Outside CS, I would also recommend taking at least one foreign language to give you a little more basis in how syntax and grammar vary across languages. A non-latin based language, like Japanese, might help.
    • I agree with the parent. If I were a potential future employer or if I sat on an admissions committe for grad school, I'd look at your "custom degree" and say this to myself:

      "Here's a kid who wanted to get out of having to take the hard CS classes, so he made up a major that sounds sexy and then slacked off. He just couldn't hack the real CS program."

      Now, I'm not saying that this is you. If this would be a mistaken perception of what you're doing, I advise you to not invite the mistake, prove to everyon

  • is this really appropriate for an undergrad degree (Score:4, Insightful)

    by blackcoot ( 124938 ) on Monday November 07, 2005 @07:51PM (#13974872)
    i don't believe that your chosen major is sufficiently broad to be useful. i also believe that, unless you intend to immediately pursue a graduate degree, you're likely to find it very hard to explain to potential employers what, exactly, it is that your degree says you can do. i don't know that anyone can develop the degree of maturity in the fairly wide set of disciplines that you'll have to master (general comp. sci., intro a.i., "soft" a.i., statistics, linguistics, and quite possibly some signal processing) to really succeed at this major as an undergrad. i suggest instead that you do a major in comp. sci. (or e.e., or linguistics) with a concentration in computational linguistics and then pursue further study at the graduate level.
    • i think you're a bit off the mark. normally as you say someone
      would get a cs degree, and do additional work in comp ling, then
      apply to grad school with an specific bias towards that kind
      of work.

      however, if you really know thats what you want to do, there's alot
      to be gained by putting together an interdiscplenary degree
      based around your interests and getting it approved. it would
      mean you would be able to take more courses in linguistics and
      philosophy of language, or statistical methods, or logic, or
      whatever
      • I think that it wouldn't be terribly difficult to move from a computational linguistics job to a software engineering job. As long as you get a fair amount of training in software design (what do you think you'll be doing with a PhD in computational linguistics? Creating databases by hand for a grad student?) and general theory of computation, you should be able to transfer most of your skills.

        The only issue is language specialization--if you only know Prolog very well, you might have trouble switching to J

  • Markov Chaining (Score:4, Interesting)

    by conJunk ( 779958 ) on Monday November 07, 2005 @08:01PM (#13974962)
    Markov Chaining [wikipedia.org], as applied to language, is really interesting.

    It involves taking a canon of text (for example, the complete works of shakespeare, or every example of written english you can get your hands on, or every example of transcripts of spoken english you can get your hands on) and subjecting them to a statistical analysis of what chucks (that is, words, phrases, what have you) are likely to follow which other chunks.

    while the outputs tend to make little sense, it is "interesting" to see what kinds of "statistically probably" examples of language a computer can make based on the training (input) you've given it

  • You May be a Cunning Linguist (Score:3, Funny)

    by Anonymous Coward on Monday November 07, 2005 @08:43PM (#13975257)
    But I am a Master Debator
  • The courses you want are: Intro to AI; NLP; Linguistics; Machine Learning; Discourse Processing; Dialogue Processing; possibly Speech Recognition or Machine Translation.

    For more ideas, see the list of courses offered at CMU's Language Technologies Institute, and pick the introductory ones.

    To all those saying "why do this in undergrad?", here's why: because a Bachelors degree doesn't matter a whit. You could have a BA in English, and go on to get a job in software engineering; you could have a BS in Chemistr

    • "You could have a BA in English, and go on to get a job in software engineering; you could have a BS in Chemistry, and go on to work as a journalist."

      That's not necessarily true. It's true that you could easily find yourself working in a field that you didn't study as an undergrad, but I think you'll find that it's difficult to make the transition to a STEM (science, technology, engineering, and mathematics) field if you didn't major in one of these things. Ten years ago, you could find a job in soft

    • You are assuming school and degrees are for show, for others. They are primarily for you. That is why, in the end, it is really more important that you got good education than that you went to a big name school.
  • When I present my proposal for this degree to the school's advising staff, I would like to have a complete list of all of the topics this major should cover.

    Something to keep in mind whenever you want to have a group of people approve of something is to already know that they will. Start talking to the advising staff. In particular start talking to professors. They would need to approve, and for a custom major you'll likely need an adviser. Who on the faculty do you think would be appropriate? Approach t

  • Don't Make Your Own (Score:2, Insightful)

    by coaxial ( 28297 )
    Make-a-Degree programs are for underwater basket weaving [berkeley.edu]. Computational linguistics is an advanced topic. You won't touch it, nor should you, until a graduate program. You don't know enough about computer science to do anything advanced yet. Get yourself a CS degree and take some linguistic, anthropology, and psychology electives, then apply to a graduate program and do CL as your thesis. Read some CL papers and apply to the schools that publish in the CL journals. University of Washington has a progr [washington.edu]
  • I had a prof for discrete math that work heavily on linguistice reseach... check out some of his papers and contact him for more info.
      http://www.csc.ncsu.edu/faculty/rodman/ [ncsu.edu]
  • I'm in my last year before becoming a Chemical Engineer, and I'm specializing in polymers but I have taken an interest in CS also (mostly as a hobby). I'm ok about learning a programming language by myself... but some pointers as to which language is best wouldn't hurt me.

    What do scientists or engineers use when they want to simulate a reactor? How about design of equipment? Does everyone use Excel and Macros/Solver? Or Aspen???

    Today I handed in a paper which did the design and pricing of a distillation
  • The University of Regensburg, Germany (and some others in .de, e.g. Konstanz) offer "Information Science" which may be pretty close to what you want. It contains scanning and parsing, information retrieval, information analysis on syntactical, semantical and pragmatical level as well as associated courses. I'm writing my PhD there right now and don't regret it.

    Feel free to mail me for more information!

  • Speaking as the director of a new professional MA program in computational linguistics [washington.edu] at the University of Washington, I'd say the answer depends on what you plan to do next, and what specific computational linguistics courses are available there at MIT.

    Successful computational linguists have strong programming skills, a deep understanding of algorithms and/or systems architecture, and a linguist's perspective on language, linguistic patterns and linguistic structures. An understanding of machine learni

  • take tons of it. learn first hand why the rules of language are far too complex to machine...
  • The problem with specializing too early is that students risk getting bored and wanting to change to a different degree. You said it yourself, you're doing bioengineering and you want to do programming. Doing a solid CS degree as mentioned earlier then specializing later on either by choosing final year electives or doing masters with an linguistics focus would be more flexible.

    Your job prospects with a degree like this would probably be a lot worse than most CS graduates. It would be very hard for you to g
  • I minor'ed in NLP for my Masters at edinburgh this year, the statistical stuff is where its at, specifically hidden markov modelling of speech units. Its truly remarkable what you can do with only TNT (trigrams n tags) which as the name suggests is a trigram tagger.

    Stay the hell away from formal semantic modelling..its horrible.

    You can access all edinburghs course informations via http://inf.ed.ac.uk/ [ed.ac.uk] if you want to see the kinds of things covered in each course.

    I did Introduction to Computational Linguisti

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