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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Biçimbilimsel Çözümleme

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KOC UNIVERSITY
ELECTRICAL AND COMPUTER ENGINEERING
ECOE 590 SEMINAR
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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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CFP: Parser Evaluation using Textual Entailments (PETE)

SemEval-2010 Shared Task #12
Parser Evaluation using Textual Entailments (PETE)
(http://pete.yuret.com)

The purpose of this post is to encourage participation in the task "Parser Evaluation using Textual Entailments" in the 5th International Workshop on Semantic Evaluations, SemEval-2010 (http://semeval2.fbk.eu/semeval2.php) collocated with ACL-2010, July
15-16, Uppsala.

This shared task should be of interest to researchers working on
* parsing
* semantic role labeling
* recognizing textual entailments

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 (RTE) based on syntactic information. Given two text fragments called 'Text' and 'Hypothesis', Textual Entailment Recognition is the task of determining whether the meaning of the Hypothesis is entailed (can be inferred) from the Text. The PETE task focuses on entailments that can be inferred using syntactic information alone.

• Text: The man with the hat was tired.


• Hypothesis-1: The man was tired. (YES)



• Hypothesis-2: The hat was tired. (NO)

Our goals in introducing this task are:

• To focus parser evaluation on semantically relevant phenomena.


• To introduce a parser evaluation scheme that is formalism independent.


• To introduce a targeted textual entailment task focused on a single linguistic competence.
  


• To be able to collect high quality evaluation data from untrained annotators.

The following criteria were used when constructing the entailments:

• They should be decidable using only syntactic inference.


• They should be easy to decide by untrained annotators.


• They should be challenging for state of the art parsers.

You can find more details about our entailment generation process in the PETE Guide. You can download the development and test datasets including gold answers and system scores here: PETE_gold.zip. There is no training data. The evaluation is similar to other RTE tasks. There is a Google group semeval-pete for task related messages.

Instructions:

• join the mailing list (http://groups.google.com/group/semeval-pete)
• register in SemEval website (http://semeval2.fbk.eu)
• download the development (trial) data from SemEval website (http://semeval2.fbk.eu)
• download task guide from task website (http://pete.yuret.com/guide)
• download test data from SemEval website (http://semeval2.fbk.eu)
• upload results to SemEval website (http://semeval2.fbk.eu)

Important Dates:

• February 19 - the development (trial) data available.


• March 26 - the test data available.


• April 2 - end of submission period for the task.


• April 17 - Submission of description papers.


• May 6 - Notification of acceptance.


• July 15-16 - Workshop at ACL 2010, Uppsala.
  
  

Parsers:

Here are some links for publicly available parsers that can be used in this task. You do not have to use any of these parsers, in fact you do not have to use a conventional parsing algorithm at all -- outside the box approaches are highly encouraged. However, to get a quick baseline system using an existing parser may be a good way to start.

• Berkeley Parser
• Bikel Parser
• C&C CCG Parser
• Collins Parser
• Charniak Parser
• CMU Link Parser
• DeSR Parser
  
• Enju Parser
• MaltParser
• Minipar
• MSTParser
• RASP Parser
  
• Stanford Parser
• conll-entailments.pl: This is not a parser but a simple script to illustrate how short entailments may be generated from a dependency parse. Incomplete and buggy, use at your own risk.
  

Further Reading:

• PETE Guide: A description of the entailment generation process (February, 2010).
• D09-1085.pdf: Rimell, L., S. Clark, and M. Steedman. Unbounded Dependency Recovery for Parser Evaluation. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (August, 2009).
• thesis.pdf: Onder Eker's MS thesis (August, 2009).


• semeval-abstract.pdf: The PETE task abstract (December, 2008).
• pete.pdf: The initial PETE task proposal (September, 2008).
• Workshop on Cross-Framework and Cross-Domain Parser Evaluation (August, 2008)
  
• natlog-wtep07-final.pdf: Bill MacCartney and Christopher D. Manning. 2007. Natural logic for textual inference. ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 193-200. (June, 2007).
• targeted textual entailments: On targeted textual entailments in general (June 2007).



• a blog post: On the consistency of Penn Treebank annotation (October, 2006).
• lre98.pdf: Carroll, J., E. Briscoe and A. Sanfilippo (1998) `Parser evaluation: a survey and a new proposal'. In Proceedings of the 1st International Conference on Language Resources and Evaluation, Granada, Spain. 447-454.

Contact:

• Deniz Yuret dyuret@ku.edu.tr

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Preprocessing with Linear Transformations that Maximize the Nearest Neighbor Classification Accuracy

Mehmet Ali Yatbaz and Deniz Yuret. 1st CSE Student Workshop (CSW’10), 21 February 2010, Koc Istinye Campus, Istanbul. (PDF, PPT)

Abstract
We introduce a preprocessing technique for classification problems based on linear transformations. The algorithm incrementally constructs a linear transformation that maximizes the nearest neighbor classification accuracy on the training set. At each iteration the algorithm picks a point in the dataset, and computes a transformation
that moves the point closer to points in its own class and/or away from points in other classes. The composition of the resulting linear transformations lead to statistically significant improvements in instance based learning algorithms.



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L1 Regularization for Learning Word Alignments in Sparse Feature Matrices

Ergun Bicici and Deniz Yuret. 1st CSE Student Workshop (CSW’10), 21 February 2010, Koc Istinye Campus, Istanbul. (PDF, Poster)

Abstract
Sparse feature representations can be used in various domains. We compare the effectiveness of $L_1$ regularization techniques for regression to learn mappings between features given in a sparse feature matrix. We apply these techniques for learning word alignments commonly used for machine translation. The performance of the learned mappings are measured using the phrase table generated on a larger corpus by a state of the art word aligner. The results show the effectiveness of using $L_1$ regularization versus $L_2$ used in ridge regression.

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