reading c++ code tree-distance

2h research methods + discussion about the project

1 spanish class 1h

Apply for a TCHPC accoun


http://www.tchpc.tcd.ie/support/training/#index1h2

preparing spanish lecture

reading dependence tree code.

tree-distance:

/sharde/teaching/CSLL/4th  /ZhangShasha/lib/

Bayesian Learning tutorial

Study english 1h

burocracy.

reading group

A New Approach to the study of translationese: Machine-learning the difference between original and translated text

MT text categorization

translationese: dialect

translation:

less lexical dense

order repeat linguistic features

unigram , bi gram , tri gram -> windows size n words.

lemma -> root of the word

SVM -> hav capacity of feature selection.

majority vouting

recall maximitacion (at least 1 vote).

pronouns & adverbial forms the most important.

concept:

compatible corpus: same topic.

humman comparation, same performance as machine.

Designing the SRL system,

it will be able to label nodes, and to label semantic relations,
but it will be not able to predict semantic relations

1 spanish lecture

2 h research methods

chatting about how to design the SRL system.
2 implementations

recall English lessons

13 Hidden Markov models

Introduction to machine learning.


urns example

reading mail

undestanding SRL corpus

Fwd: [SIGIR2009-Poster] Your Paper #169

We regret to inform you that your poster submission

Title: Estimating performance of text classification

has not been accepted for the SIGIR 2009 Poster Track.

The review process was extremely selective and many submissions could
not be accepted for the final program. Out of the 256 poster submissions,
the program committee selected only 86 posters, an acceptance rate of about
34%.

The reviews for your submission are included below. Each poster was
reviewed by at least three reviewers. Final poster decisions were made
by the poster co-chairs.

The conference program and registration details will be available on
the conference website shortly at:

http://www.sigir2009.org/

We hope to see you in Boston in July. If you plan on attending the
conference, then it is important to note that all visitors to the US require
a visa or a visa waiver. The starting point is the page
https://esta.cbp.dhs.gov/esta/esta.html,
through which citizens of many countries can obtain a visa waiver.

Thank you again for submitting your poster to SIGIR2009-Poster.

Best regards,

The SIGIR2009-Poster Program Chairs
Jimmy Lin and Don Metzler
------------- Review from Reviewer 1 -------------
Relevance to SIGIR (1-5, accept threshold=3)  : 3
Originality of work (1-5, accept threshold=3)  : 3
Quality of work (1-5, accept threshold=3)     : 3
Adequacy of Citations (1-5, accept threshold=3) : 3
Quality of presentation (1-5, accept threshold=3) : 3
Impact of Ideas or Results (1-5, accept threshold=3) : 2
Impact of Resources (1-5, no threshold)       : 1
Recommendation (1-6)                          : 2
Confidence in review (1-4)                    : 2

-- Comments to the author(s):
This paper takes a look at the relationship between the number of
classes and accuracy regarding classfication in multiple classes.

Note that some of the equations came out as gibberish (in Adobe Reader
7) - i.e. some of the text in the last para of Section 2.
-- Summary:
This seems like a small work toward looking at text classifier
performance.  For me, it seemed more like a core dump of bits of
information.  I would recommend that the authors try to make it clear
what the real contribution of this paper is in the future.
---------- End of Review from Reviewer 1 ----------
------------- Review from Reviewer 2 -------------
Relevance to SIGIR (1-5, accept threshold=3)  : 4
Originality of work (1-5, accept threshold=3)  : 3
Quality of work (1-5, accept threshold=3)     : 2
Adequacy of Citations (1-5, accept threshold=3) : 3
Quality of presentation (1-5, accept threshold=3) : 2
Impact of Ideas or Results (1-5, accept threshold=3) : 2
Impact of Resources (1-5, no threshold)       : 1
Recommendation (1-6)                          : 2
Confidence in review (1-4)                    : 4

-- Comments to the author(s):
The paper studied the relationship between the number of classes and
the classification accuracy. The problems of the paper are listed
below.

(1) For multi-class classification, the numbers of samples in
different categories are often very imbalanced, which can have big
effects on the classification accuracy. However, this important factor
is ignored in the paper.

(2) In Figure 1, it is not clear to judge that naïve Bayes performed
better than kNN.

(3) The presentation is not good. Section 2 needs to be well
re-organized and greatly polished. The English needs much improvement.

-- Summary:
There is a big technical problem in the paper, and the presentation is bad.
---------- End of Review from Reviewer 2 ----------
------------- Review from Reviewer 3 -------------
Relevance to SIGIR (1-5, accept threshold=3)  : 3
Originality of work (1-5, accept threshold=3)  : 1
Quality of work (1-5, accept threshold=3)     : 1
Adequacy of Citations (1-5, accept threshold=3) : 1
Quality of presentation (1-5, accept threshold=3) : 1
Impact of Ideas or Results (1-5, accept threshold=3) : 1
Impact of Resources (1-5, no threshold)       : 1
Recommendation (1-6)                          : 1
Confidence in review (1-4)                    : 6

-- Comments to the author(s):
The poster analyses the relation between the expected accuracy of
classifiers and  the number of classes.

It describes an incremental algorithm for estimating the accuracy of
classifiers for a given classification problem.

Some experiments are performed on a small dataset.

The paper is not understandable in its present form and should be
rewritten. Definitions should be provided (e.g. epistasis or synergy
of a split), the algorithm should be carefully described.

-- Summary:
Paper should be completely rewritten. The present version cannot be understood.
---------- End of Review from Reviewer 3 ----------

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reading 50 emails.

two hours meeting on semantic role labeling research group