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ブックマーク / nlpers.blogspot.com (9)
●
2 users
●
nlpers.blogspot.com
●テクノロジー
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) If you compare vision research withNLP research, there are a lot of interesting parallels. Like we both like linear models. And conditional random fields. And our problems are a lot harder than binary classification. And there are standar
niam
2009/07/09
●nlp
●machine learning
●*あとで読む
●
6 users
●
nlpers.blogspot.com
●テクノロジー
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
niam
2009/07/02
●machine learning
●nlp
●research
●mustread
●*あとで読む
●
4 users
●
nlpers.blogspot.com
●テクノロジー
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
niam
2009/06/28
●nlp
●ssl
●machine learning
●research
●*あとで読む
●
4 users
●
nlpers.blogspot.com
●学び
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 itis, or at least what the input/output is. It's like the problem
niam
2009/06/17
●nlp
●marketing
●*あとで読む
●
6 users
●
nlpers.blogspot.com
●学び
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I hope this post will be a small impetus to get other people to post comments about papers they saw at NAACL (and associated workshops) that they really liked. As usual, I stayed for the whole conference, plus workshops. As usual, I also h
niam
2009/06/14
●research
●mustread
●nlp
●*あとで読む
●
3 users
●
nlpers.blogspot.com
●学び
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) As some of you know, I run a (machine learning) reading group every semester. This summer we're doing "assorted" topics, which basically means students pick a few papers from the past 24 months that are related and present on them. The wee
niam
2009/06/11
●nlp
●*あとで読む
●research
●mustread
●
5 users
●
nlpers.blogspot.com
●テクノロジー
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) The standard classification setting is a input distribution p(X) and a label distribution p(Y|X). Roughly speaking, domain adaptation (DA) is the problem that occurs when p(X) changes between training and test. Transfer learning (TL) is th
niam
2009/05/28
●machine learning
●nlp
●
3 users
●
nlpers.blogspot.com
●テクノロジー
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) I've been slow at posting because I wanted to post on this current topic for a while, but wanted to work out some more details before doing so. Well, itdidn't happen. So I'm writing sans details. Let's think for a minute about non-linear
niam
2009/04/28
●nlp
●research
●mustread
●*あとで読む
●
11 users
●
nlpers.blogspot.com
●テクノロジー
my biased thoughts on the fields of natural language processing (NLP), computational linguistics (CL) and related topics (machine learning, math, funding, etc.) Some of you know that I (at least used to be) a bit of a programming language snob. In fact, on several occasions, I've met (inNLP or ML land) someone who recognizes my name from PL land and is surprised that I'm not actually a PL person.
niam
2009/03/27
●research
●mustread
●まとめ
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設定を変更しましたx
Small changes beget good or bad examples?
ICML/COLT/UAI 2009 retrospective
Should there be a shared task for semi-supervised learning in NLP?
NL Generation: A new problem
NAACL-HLT 2009 Retrospective
The importance of input representations
Domain adaptation vs. transfer learning
Viterbi search, minimum Bayes risk and laplacian eigenmaps
Programming Language of Choice