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It's really strange to look back now over the past ten to fifteen years and see a very small pendulum that no one really cares about swing around. I've spent the last ten years trying to convince you that structured prediction is RL; now I'm going to tell you that was a lie :). Short Personal History Back in 2005, John Langford, Daniel Marcu and I had a workshop paper at NIPS on relating structure
I ran a grad seminar in reinforcement learning this past semester, which was a lot of fun and also gave me an opportunity to catch up on some stuff I'd been meaning to learn but haven't had a chance and old stuff I'd largely forgotten about. It's hard to believe, but my first RL paper was eleven years ago at a NIPS workshop where Daniel Marcu, John Langford and I had a first paper on reducing stru
About a month ago, the paper Bag of Tricks for Efficient Text Categorization was posted to arxiv. I found it thanks to Yoav Goldberg's rather incisive tweet: Yoav is basically referring to the fact that the paper is all about (a) hashing features and (b) bigrams and (c) a projection that doesn't totally make sense to me, which (a) vw does by default (b) requires "--ngrams 2" and (c) I don't totall
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I can probably count on my fingers the number of papers I've submitted for which a reviewer hasn't complained about a baseline in some way. I don't mean to imply that all of those complaints are invalid: many of them were 100% right on in
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I'm going to try something new and daring this time. I will talk about papers I liked, but I will mention some things I think could be improved. I hope everyone finds this interesting and productive. As usual, I didn't see everything, didn
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) In terms of (importance divided-by glamour), hyperparameter (HP) search is probably pretty close to the top. We all hate finding hyperparameters. Default settings are usually good, but you're always left wondering: could I have done better
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) When I was an undergrad, probably my favorite CS class I took was algorithms. I liked it (a) because my background was math so it was the closest match to what I knew and (b) because even though it was "theory," a lot of the stuff we learn
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Usual caveats: didn't see all papers, blah blah blah. Also look for #acl14nlp on twitter -- lots of papers were mentioned there too! A Tabular Method for Dynamic Oracles in Transition-Based Parsing; Yoav Goldberg, Francesco Sartorio, Giorg
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I feel a bit odd doing my "what I liked at NAACL 2013" as one of the program chairs, but not odd enough to skip what seems to be the most popular type of post :). First, though, since Katrin Kirchhoff (my co-chair) and I never got a chance
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I had a post a while back on teaching ML that I still basically stand by. I've taught intro grad NLP here at UMD twice now, and a sort-of-similar-course back at Utah once. I find these courses really hard to teach. And not for the usual
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) NIPS is over as of last night. Overall I thought the program was strong (though I think someone, somewhere, is trying to convince me I need to do deep learning -- or at least that was the topic d'jour... or I guess d'an? this time). I wa
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Like many people, I spent last week in lovely Montreal (at least lovely for the second half) at NAACL. Despite the somewhat unfortunate submission date, I thought the program this year was quite good. Of course I didn't see every talk an
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) As Daniel Hsu and John Langford pointed out recently, there has been a lot of recent progress in active learning. This is to the point where I might actually be tempted to suggest some of these algorithms to people to use in practice, for
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I'm using ACL/ICML as an excuse to jumpstart my resumed, hopefully regular, posting. The usual "I didn't see/read everything" applies to all of this. My general feeling about ACL (which was echoed by several other participants) was that
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Someone (can't remember who anymore, even though it was just a couple days ago!) pointed out to me that Google scholar seems to be doing weird things with citations. In particular, it seems to think that the citation relation is symmetric
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Happy New Year and I know I've been silent but I've been busy. But no teaching this semester (YAY!) so maybe you'll see more posts. At any rate, I'm really late to the table, but here are my comments about this past year's NIPS. Before w
timv said... I've definitely felt the IO bottleneck before.. the best solution is to get a really beefy 64-bit multi-core machine with tons of memory to run all your training on. This way you can leave everything in memory.. no more worries! Have you considered posing this as a question on MetaOptimize (http://metaoptimize.com/qa)? 31 August, 2010 22:24 John Langford said... I've seen VW work pret
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) ACL 2010 finished up in Sweden a week ago or so. Overall, I enjoyed my time there (the local organization was great, though I think we got hit with unexpected heat, so those of us who didn't feel like booking a room at the Best Western --
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Just got back from Israel for ICML, which was a great experience: I'd wanted to go there for a while and this was a perfect opportunity. I'm very glad I spent some time afterwards out of Haifa, though. Overall, I saw a lot of really good s
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I just returned from NAACL 2010, which was simultaneously located in my home town of Los Angeles and located nowhere near my home town of Los Angeles. (That's me trying to deride downtown LA as being nothing like real LA.) Overall I was pl
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I've had discussions about this with tons of people, and it seems like my approach is fairly odd. So I thought I'd blog about it because I've put a lot of thought into it over the past four offerings of the machine learning course here at
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) In the context of something completely unrelated, I was looking for a fairly general pattern in the Google 1TB corpus. In particular, I was looking for verbs that are sort of transitive. I did a quick grep for 5grams of the form "the SOMET
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) This isn't so much a post in the "GSI" series, but just two links that recently came out. Kevin Knight and Philip Resnik both just came out with tutorials for Bayesian NLP. They're both excellent, and almost entirely non-redundant. I highl
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Well, ACL and EMNLP are long gone. And sadly I missed one day of each due either to travel or illness, so most of my comments are limited to Mon/Tue/Fri. C'est la vie. At any rate, here are the papers I saw or read that I really liked. P09
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I really enjoyed Mark Dredze's talk at EMNLP on multiclass confidence weighted algorithms, where they take their CW binary predictors and extend them in two (basically equivalent) ways to a multiclass/structured setting (warning: I haven't
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) There is a cool view of the whole non-parametric Bayes thing that I think is very instructive. It's easiest to see in the case of the Pitman-Yor language modeling work by Frank Wood and Yee Whye Teh. The view is "memorize what you can, and
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) This will probably be a bit briefer than my corresponding NAACL post because even by day two of ICML, I was a bit burnt out; I was also constantly swapping in other tasks (grants, etc.). Note that John has already posted his list of papers
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) (Guest post by Kevin Duh. Thanks, Kevin!) At the NAACL SSL-NLP Workshop recently, we discussed whether there ought to be a "shared task" for semi-supervised learning in NLP. The panel discussion consisted of Hal Daume, David McClosky, and
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Those who talk to me a lot over the years know that I really think that generation is a cool and interesting problem, but one that is hampered by a lack of clarity of what it is, or at least what the input/output is. It's like the problem
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