İngilizce Türkçe otomatik tercüme

Google sonunda otomatik tercüme yaptığı dillere Türkçe'yi de
ekledi. Denemek isterseniz: http://translate.google.com

Bu teknolojinin İngilizce bilmeyen Türk nüfusunun internetteki
bilgi birikimine ulaşımı için önemli olduğunu düşünüyor ve birkaç
yıldır üzerinde ben de çalışıyorum. En büyük engellerden biri
araştırma amacıyla kullanılabilecek yüklü miktarda
İngilizce-Türkçe paralel metne ihtiyaç olması (yaklaşık 100 milyon
kelime = 1000 kitap). Bu metni toplayabilmek için bir iki yıl
telefonla devlet kurumları, uluslararası kuruluşlar, yayınevi,
haber kurumu, hukuk ve tercümanlık şirketleri, üniversite
bölümleri vs ile görüşüp pozitif bir cevap alamayınca yoruldum ve
vazgeçtim. İşin üzücü tarafı karşılaştığım büyük engelin yayın
hakkı, fikir mülkiyeti gibi hukuksal bir konu değil, insanların
ilgisizliği olması. Şimdilik bir iki milyon kelimelik metinden
geliştirilmiş oyuncak bir sistemle uğraşıyorum öğrencilerimle.
Google'ın sistemini ben yazmış olmak isterdim. Ama henüz yarışma
bitmiş değil, sistemin kalitesine bir örnek olarak bu paragrafın
bir tercümesini veriyorum...

This technology does not speak English in the Turkish population
on the Internet access to knowledge is important to think and a
few years, I am working on. One of the biggest obstacles can be
used for research purposes in the amount of installed
English-English parallel text is needed (about 100 million word =
1000 books). This text to be able to collect a two-year phone and
the state institutions, international organizations, publishing,
news agency, law and translation companies, universities sections
and with a positive answer so do not get tired and I've given
up. The major obstacle faced by the unfortunate job of
broadcasting the The right to legal issues such as property, not
the ideas, people is indifference. Currently, one of two million
words of text I'm dealing with a system developed for students
with toys. I would like it to Google's system. But the contest
yet not finished, as an example of the quality of the system of
this paragraph I give a translation...


Full post... Related link

Neşe Aral, M.S. 2009

Current position: Asst. Prof., Türkisch-Deutsche Universität, Istanbul. (email, webpage1, webpage2, webpage3, webpage4).
M.S. Thesis: Dynamics of Gene Regulatory Cell Cycle Network in Saccharomyces Cerevisiae. Koç University Department of Physics, January 2009. (Download PDF).



Abstract:
In this thesis, the genetic regulatory dynamics within the cell cycle network of the yeast Saccharomyces Cerevisiae is examined. As the mathematical approach, an asynchronously updated Boolean network is used to model the time evolution of the expression level of genes taking part in the regulation of the cell-cycle. The attractors of the model’s dynamics and their stability are investigated by means of a stochastic transition matrix. It is shown that the cell cycle network has unusual dynamical properties when compared with similar random networks. Furthermore, an entropy measure is employed to monitor the sequential evolution of the system. It is observed that the experimentally identified cell cycle phases G1, S, G2 and M correspond to the stages of the network where the entropy goes through a local extremum.
Full post... Related link

Show and Tell

Here is a little demo video of some work I did with Sajit at MIT CSAIL this summer. The computer watches us play with a ball and produces live commentary. For now the detection of actions (like give, drop, move) are hand coded, the next step would be to learn them from examples. The step after that is "tell and show", i.e. to go from words to pictures. This would complete the imagination-perception loop which may underlie much understanding and problem solving.

I think one of the coolest things about the current implementation is how the computer starts a sentence and cuts it in half to say something more important. There is always tons of possible things to say and possible words to say them with, and a similar competition must be going on in our heads.


Full post...

Morphological cues vs. number of nominals in learning verb types in Turkish: Syntactic bootstrapping mechanism revisited

Deniz Yuret, A. Engin Ural, Nihan Ketrez, Dilara Koçbaş and Aylin C. Küntay. In The Boston University Conference on Language Development (BUCLD) (Long abstract, poster, PDF)

Abstract: The syntactic bootstrapping mechanism of verb classification was evaluated against child-directed speech in Turkish, a language with rich morphology, nominal ellipsis and free word order. Machine-learning algorithms were run on transcribed caregiver speech (12,276 and 20,687 utterances) directed to two Turkish learners (one hour every two weeks between 0,9 to 1;10) of different socioeconomic backgrounds. The corpora contained 12,276 and 20,687 child-directed utterances. Study 1 found that the number of nominals in child-directed utterances plays some role in classifying transitive and intransitive verbs. Study 2 found that accusative morphology on the noun is a stronger cue in clustering verb types. Study 3 found that verbal morphology is useful in distinguishing between different subtypes of intransitive verbs. These results suggest that syntactic bootstrapping mechanisms should be extended to include morphological cues to verb learning in morphologically rich languages.

Full post... Related link

Parser Training and Evaluation using Textual Entailments

A task proposal for SemEval-2010 by Deniz Yuret and Önder Eker. For example sentences and the bibliography, please download the original PDF.

Description of the Task

We propose a targeted textual entailment task designed to train and evaluate parsers (PETE). The typical parser training and evaluation methodology uses a gold treebank which raises several issues: (1) The treebank is built around a particular linguistic representation which makes parsers that use different representations (e.g. phrase structure vs. dependency) difficult to compare. (2) The parsers are evaluated based on how much of the linguistic structure dictated by the treebank they can replicate, some of which may be irrelevant for downstream applications. (3) The annotators that create the treebank not only have to understand the sentences in the corpus, but master the particular linguistic representation used, which makes their training difficult and leads to inconsistencies (see Carroll1998 for a review and Parseval2008 for more recent work on parser evaluation).

In the proposed method, simple textual entailments like the following will be used to fine-tune and evaluate different parsers:

• Final-hour trading accelerated to 108.1 million shares, a record for the Big Board.
  
  • 108.1 million shares was a record. -- YES
    
  • Final-hour trading accelerated a record. -- NO
    
  
  
• Earlier the company announced it would sell its aging fleet of Boeing Co. 707s because of increasing maintenance costs.
  
  • It would sell the fleet because of increasing costs. -- YES
    
  • Selling the fleet would increase maintenance costs. -- NO
    
  
  
• Persistent redemptions would force some fund managers to dump stocks to raise cash.
  
  • The managers would dump stocks to raise cash. -- YES
    
  • The stocks would raise cash. -- NO

The entailment examples will be generated based on the following criteria: (1) It should be possible to automatically decide which entailments are implied based on the parser output only, i.e. there should be no need for lexical semantics, anaphora resolution etc. (2) It should be easy for a non-linguist annotator to decide which entailments are implied, reducing the time for training and increasing inter-annotator agreement. (3) The entailments should be non-trivial, i.e. they should focus on areas of disagreement between current state of the art parsers. The above examples satisfy all three criteria.

Training and evaluating parsers based on targeted textual entailments address each of the issues listed in the first paragraph regarding treebank based methods: The evaluation is representation independent, therefore there is no difficulty in comparing the performance of parsers from different frameworks. By focusing on the parse differences that result in different entailments, we ignore trivial differences that stem from the conventions of the underlying representation which should not matter for downstream applications. Finally our annotators will only need a good understanding of the English language and no expertise on any linguistic framework.

Generating Data

The example entailment questions can be generated by considering the differences between the outputs of different state of the art parsers and their gold datasets. Some of the detected parse differences can be turned into different entailments about the sentence. The example entailments in the previous section were generated comparing the outputs of two dependency parsers, which are included in the appendix. The generated entailments will then be annotated by multiple annotators and the differences will be resolved using standard techniques.

Generating entailment questions out of parser differences allow us to satisfy conditions 1 and 3 easily: the entailments can be judged based on parser output because that is how they were generated, and they are non-trivial because some state of the art parsers disagree on them.

In our experience the most difficult condition to satisfy is 2: that it should be easy for a non-linguist annotator to decide which entailments are implied. In most of the example sentences we looked at, the differences between the parsers were trivial, e.g. different conventions on how to tag coordinating conjunctions, or whether to label a particular phrase with ADVP vs. ADJP etc. These differences are trivial in the sense that it is impossible to generate different entailments from them, thus it is hard to see how they would matter in a downstream application.

The trivial differences between parsers make example generation using our process difficult. The efficiency of example generation may be improved by pre-filtering candidate sentences which contain structures that involve non-trivial decisions by the parser such as prepositional phrase attachments. In addition some types of entailment generation can be automated. On the other hand the requirement of expressing differences in entailments will hopefully focus the training and the evaluation on non-trivial differences that actually matter in applications.

Evaluation Methodology

The participants will be provided with training and test sets of entailments and they will be evaluated using the standard tools and methodology of the RTE challenges (Dagan2006). The main difference is that our entailment examples focus exclusively on parsing. This should make it possible to write simple tree matching modules that decide on the entailments based on parser output alone. Example tree matching modules for standard formats (Penn Treebank (Marcus1993) format for phrase structure parsing, and CoNLL (Nivre2007) format for dependency parsing) will be provided as examples which should make preparing an existing parser for evaluation relatively easy.

The training part will be more parser specific, so individual participants will have to decide how to best make use of the provided entailment training set. It is unlikely that we will be able to generate enough entailment examples to train a parser from scratch. Therefore the task will have to be open to using other resources. The participants will be free to use standard resources such as treebanks to train their parsers. We can also consider restricting the outside training resources (e.g. Penn Treebank only) and the domain of the entailments (e.g. finance only). The entailment training set can then be used to fine tune the parser by focusing the evaluation on important parser decisions that effect downstream applications.

Full post... Related link