Mehmet Ali Yatbaz and Deniz Yuret. Coling 2010. pp. 1391--1398. Beijing, China. (PDF, Poster)
Abstract: We show that unsupervised part of speech tagging performance can be significantly improved using likely substitutes for target words given by a statistical language model. We choose unambiguous substitutes for each occurrence of an ambiguous target word based on its context. The part of speech tags for the unambiguous substitutes are then used to filter the entry for the target word in the word--tag dictionary. A standard HMM model trained using the filtered dictionary achieves 92.25% accuracy on a standard 24,000 word corpus.
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August 26, 2010
July 15, 2010
SemEval-2010 Task 12: Parser Evaluation using Textual Entailments
Deniz Yuret, Aydın Han, Zehra Turgut. Proceedings of the 5th International Workshop on Semantic Evaluation. (SemEval-2010) pp. 51--56. July, 2010. Uppsala, Sweden. (PDF, Presentation, Task website, Proceedings, Journal submission).
Abstract: Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based on syntactic information alone. PETE introduces a new parser evaluation scheme that is formalism independent, less prone to annotation error, and focused on semantically relevant distinctions.
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Abstract: Parser Evaluation using Textual Entailments (PETE) is a shared task in the SemEval-2010 Evaluation Exercises on Semantic Evaluation. The task involves recognizing textual entailments based on syntactic information alone. PETE introduces a new parser evaluation scheme that is formalism independent, less prone to annotation error, and focused on semantically relevant distinctions.
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L1 Regularized Regression for Reranking and System Combination in Machine Translation
Ergun Bicici, Deniz Yuret. Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR. pp. 282--289. July 2010. Uppsala, Sweden. (PDF, Slide, Poster)
Abstract: We use L1 regularized transductive regression to learn mappings between source and target features of the training sets derived for each test sentence and use these mappings to rerank translation outputs. We compare the effectiveness of L1 regularization techniques for regression to learn mappings between features given in a sparse feature matrix. The results show the effectiveness of using L1 regularization versus L2 used in ridge regression. We show that regression mapping is effective in reranking translation outputs and in selecting the best system combinations with encouraging results on different language pairs.
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Abstract: We use L1 regularized transductive regression to learn mappings between source and target features of the training sets derived for each test sentence and use these mappings to rerank translation outputs. We compare the effectiveness of L1 regularization techniques for regression to learn mappings between features given in a sparse feature matrix. The results show the effectiveness of using L1 regularization versus L2 used in ridge regression. We show that regression mapping is effective in reranking translation outputs and in selecting the best system combinations with encouraging results on different language pairs.
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April 22, 2010
The Trouble with Physics by Lee Smolin
Smolin's book made me think of a dilemma I face often. I find the current system of scientific funding disturbing. Chief among the "values" of a scientist is absolute honesty. Yet, the project proposals we need to fill periodically ask us to describe what we are going to do in detail for the next three years. I don't know what I am going to do during the next three weeks! It depends on what results I am going to get using my current approach during the next couple of days. Maybe I will have a brilliant idea that will change my whole approach to the problem. Maybe I will be taken over with another problem. Honestly I don't know. The only thing I can promise is that I will put all my working energy on making progress on the problem that I find most promising at the time. But apparently that is not enough to get funding, and we are forced to either (i) bend the truth, or (ii) tie ourselves to an approach that we will most likely find suboptimal in the near future.
To me the answer is simple: scientists should be funded not on promises about the future (which nobody can honestly make, let alone scientists whose job is to explore the unknown), but on past performance. That leaves the problem of young scientists who have no past. There should be a reasonable amount of seed funding for such people, just enough to make sure an adventurous spirit has enough time to risk his career tackling an important and deep problem.
Smolin's book should be required reading by all who manage scientists and scientific funding. If you are not interested in the debate on string theory, just read the last few chapters on how science works based on a shared ethic, and why we should take a bit more risk on "Seers" who tend to obsess about high risk problems and may take a long time (sometimes forever) producing anything valuable.
Chapter 17 proposes the shared ethic among scientists rather than some abstract "scientific method" as chiefly responsible for the success of science. Chapter 18 draws a distinction between two types of scientists "Seers" and "Craftspeople". In fact pretty much the whole book is an elaboration of how and why the scientific establishment does not provide enough room for "Seers" who by nature like to obsess about high risk problems and need much longer incubation times.
I find the shared ethic of science to be one of the most important creations of human culture. I had long held the view that science was about "what is" and not about "what ought to be", thus science and ethics had nothing to do each other. Recently I started to see the ethic of scientists as people, if not the result of their work, as being very relevant. Dennis Overbye describes it best:
"Not only does science not provide any values of its own, say its detractors, it also undermines the ones we already have, devaluing anything it can’t measure, reducing sunsets to wavelengths and romance to jiggly hormones. It destroys myths and robs the universe of its magic and mystery. So the story goes. But this is balderdash. Science is not a monument of received Truth but something that people do to look for truth. That endeavor, which has transformed the world in the last few centuries, does indeed teach values. Those values, among others, are honesty, doubt, respect for evidence, openness, accountability and tolerance and indeed hunger for opposing points of view."
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To me the answer is simple: scientists should be funded not on promises about the future (which nobody can honestly make, let alone scientists whose job is to explore the unknown), but on past performance. That leaves the problem of young scientists who have no past. There should be a reasonable amount of seed funding for such people, just enough to make sure an adventurous spirit has enough time to risk his career tackling an important and deep problem.
Smolin's book should be required reading by all who manage scientists and scientific funding. If you are not interested in the debate on string theory, just read the last few chapters on how science works based on a shared ethic, and why we should take a bit more risk on "Seers" who tend to obsess about high risk problems and may take a long time (sometimes forever) producing anything valuable.
Chapter 17 proposes the shared ethic among scientists rather than some abstract "scientific method" as chiefly responsible for the success of science. Chapter 18 draws a distinction between two types of scientists "Seers" and "Craftspeople". In fact pretty much the whole book is an elaboration of how and why the scientific establishment does not provide enough room for "Seers" who by nature like to obsess about high risk problems and need much longer incubation times.
I find the shared ethic of science to be one of the most important creations of human culture. I had long held the view that science was about "what is" and not about "what ought to be", thus science and ethics had nothing to do each other. Recently I started to see the ethic of scientists as people, if not the result of their work, as being very relevant. Dennis Overbye describes it best:
"Not only does science not provide any values of its own, say its detractors, it also undermines the ones we already have, devaluing anything it can’t measure, reducing sunsets to wavelengths and romance to jiggly hormones. It destroys myths and robs the universe of its magic and mystery. So the story goes. But this is balderdash. Science is not a monument of received Truth but something that people do to look for truth. That endeavor, which has transformed the world in the last few centuries, does indeed teach values. Those values, among others, are honesty, doubt, respect for evidence, openness, accountability and tolerance and indeed hunger for opposing points of view."
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February 23, 2010
Biçimbilimsel Çözümleme
*********************************************
KOC UNIVERSITY
ELECTRICAL AND COMPUTER ENGINEERING
ECOE 590 SEMINAR
*********************************************
Date : 23 February 2010, Tuesday
Time : 17:00
Place : ENG B29
Title : Biçimbilimsel Çözümleme
Speaker : Gülşen Cebiroğlu Eryiğit
Download : PDF
Doğal Dil İşleme’nin en temel seviyelerinden biri olan biçimbilimsel çözümleme, bir sözcüğün yapısının bilgisayarlar tarafından otomatik olarak çözümlenmesi işlemidir. Biçimbilimsel çözümleme işlemi sonucunda bir sözcüğün en küçük anlamlı birimleri olan morfemlerin (biçimbirimlerin) bulunması ve sözcük yapısının çözümlenmesi hedeflenmektedir. Örneğin “arabalar” sözcüğünün gövdesinin “araba” olduğu ve bu sözcüğün çoğul eki almış bir isim olduğunun otomatik olarak belirlenmesi bir biçimbilimsel çözümleme işlemidir. İşlem sırasında, sözcüğü oluşturan morfemlerin birbirlerinden ayrılmasından yola çıkılarak, bu işleme aynı zamanda Biçimbilimsel Ayrıştırma adı da verilmektedir.
Bu konuşmada, biçimbilimsel çözümleme konusu ayrıntılı olarak ele alınacak, kullanım alanları, genel yaklaşımlar ve Türkçe'nin biçimbilimsel çözümlemesi konusunda bilgi verilecektir.
Full post... Related link
KOC UNIVERSITY
ELECTRICAL AND COMPUTER ENGINEERING
ECOE 590 SEMINAR
*********************************************
Date : 23 February 2010, Tuesday
Time : 17:00
Place : ENG B29
Title : Biçimbilimsel Çözümleme
Speaker : Gülşen Cebiroğlu Eryiğit
Download : PDF
Doğal Dil İşleme’nin en temel seviyelerinden biri olan biçimbilimsel çözümleme, bir sözcüğün yapısının bilgisayarlar tarafından otomatik olarak çözümlenmesi işlemidir. Biçimbilimsel çözümleme işlemi sonucunda bir sözcüğün en küçük anlamlı birimleri olan morfemlerin (biçimbirimlerin) bulunması ve sözcük yapısının çözümlenmesi hedeflenmektedir. Örneğin “arabalar” sözcüğünün gövdesinin “araba” olduğu ve bu sözcüğün çoğul eki almış bir isim olduğunun otomatik olarak belirlenmesi bir biçimbilimsel çözümleme işlemidir. İşlem sırasında, sözcüğü oluşturan morfemlerin birbirlerinden ayrılmasından yola çıkılarak, bu işleme aynı zamanda Biçimbilimsel Ayrıştırma adı da verilmektedir.
Bu konuşmada, biçimbilimsel çözümleme konusu ayrıntılı olarak ele alınacak, kullanım alanları, genel yaklaşımlar ve Türkçe'nin biçimbilimsel çözümlemesi konusunda bilgi verilecektir.
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