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

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

Full post... Related link

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.



Full post... Related link

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.

Full post... Related link

The Noisy Channel Model for Unsupervised Word Sense Disambiguation

Deniz Yuret and Mehmet Ali Yatbaz. Computational Linguistics, Volume 36, Number 1, March 2010. (Abstract, PDF)



Abstract: We introduce a generative probabilistic model, the noisy channel model, for unsupervised word sense disambiguation. In our model, each context C is modeled as a distinct channel through which the speaker intends to transmit a particular meaning S using a possibly ambiguous word W. To reconstruct the intended meaning the hearer uses the distribution of possible meanings in the given context P(S|C) and possible words that can express each meaning P(W|S). We assume P(W|S) is independent of the context and estimate it using WordNet sense frequencies. The main problem of unsupervised WSD is estimating context dependent P(S|C) without access to any sense tagged text. We show one way to solve this problem using a statistical language model based on large amounts of untagged text. Our model uses coarse-grained semantic classes for S internally and we explore the effect of using different levels of granularity on WSD performance. The system outputs fine grained senses for evaluation and its performance on noun disambiguation is better than most previously reported unsupervised systems and close to the best supervised systems.

Full post...