ABOUT KEVIN KELLY

Kevin Kelly was born in Pennsylvania in 1952. He dropped out from the university of Rhode Island after one year but he start writing for The New York Times, The Economist… In 1981 He founded the Walking Journal. He was also executive director of Wired Magazine and he won the National Magazine Award for General Execellense. elly is now editor at large for the magazine. Partially due to his reputation as Wired’s editor, he is noted as a participant and observer of “cyberculture”.

His most notable published book is Out of Control: The New Biology of Machines, Social Systems, and the Economic World (1994). The main theme of the book is that the intelligence is not organized in a central structure. The directors of the film Matrix taked some of the ideas from this book.

His famous quote: “Nobody is as smart as everybody”

Kevin Kelly (editor), In Wikipedia, The Free Encyclopedia. Retrieved November 4, 20:03: http://en.wikipedia.org/wiki/Kevin_Kelly_(editor)

WORD SENSE DISAMBIGUATION

Word Sense Disambiguation (WSD) is the work of finding the sense that a word has in a context. The main problem of the WSD is deciding what are the senses of the word. Sometimes the senses are very different or could be also very close. One of the solution to this problem that some researchers have done is to choose a dictionary and use the senses of that dictionary. WSD systems, normally, are tested and seen the results on a task and compare against those of a human.However, humans do not always agree on which sense belongs to which word. A computer is not expected to give better task than a human.

In WSD there’s two kind of approaches: the shallow approach and the deep approach. The first one happens taking the word placement and adjacent into consideration like indicators. The second one, the deep approach, that relies on dictionaries to determine the proper sense. But, unfortunately, designing a database for the deep approach is not easy.

REFERENCES:

* Word sense disambiguation. (2009, May 28). In Wikipedia, The Free Encyclopedia. Retrieved 15:59, May 28, 2009, from http://en.wikipedia.org/w/index.php?title=Word_sense_disambiguation&oldid=292903237

* Soft Word Sense Disambibiguation. Indian Institute of Technology Bombay. Retrieved 18:13, June 15, 2009, from http://www.cse.iitb.ac.in/~pb/papers/soft-wsd.pdf

* Worde Sense Disambiguation: the State of the Art. Université de Provence. Retrieved 18:21, June 15, 2009, from http://sites.univ-provence.fr/~veronis/pdf/1998wsd.pdf

* What is Word Sense Disambiguation?. In WiseGeek. Retrieved 18:24, June 15, 2009, from http://www.wisegeek.com/what-is-word-sense-disambiguation.htm

Spell Chekers

The first spell checker weren’t correctors, they were verifiers. It never gave any suggestion for the incorrect word. A Spell Checker in computing is an application program that flags those words that are not spelled correctly. The program may be capable of working on any kind of text. The spell checker works comparing individual words with the words of a dictionary. If the word is not found the program takes it as an error and suggests to be a wrong word. Sometimes when a word is not in the dictionary the spell checkers gives an option.In the dictionary that the program uses to check if the word is correct has 90.000 entries. If you write any other word out of these entries the checker would mark it like a wrong word.

REFERENCES:

*Spell checker. In Wikipedia, The Free Encyclopedia. Retrieved 18:38, April 5, 2009, from http://en.wikipedia.org/w/index.php?title=Spell_checker&oldid=294623136

*Is Spell Checking Creating a Generation of Dummies?. In Education. Retrieved 18:49, April 5, 2009, from http://www.education.com/magazine/article/spell_check/

*Milton, Roger. Spellchecking by computer. Retrieved 18:53, April 5, 2009, from http://www.dcs.bbk.ac.uk/~roger/spellchecking.html

MUSIC INFORMATION RETRIEVAL

Is the computational interdisciplinary study of music information retrieval (MIR). This science has several applications. There is a software for MIR. The human interaction and interfaces with the computer is clear with the use of the database to check the Intellectual Property rights and music (national and international), to access to musical archives, research and benchmarks databases, for the representation of the analysis and knowledge of music or for the perception affect ans emotions of the music. Also the sociological and economical music it is used. The music industry, for example, uses MIR in the production, consumption chain, distribution or validation process. It has the formal database and methods for the application to identify and recognize music or also the score or accompaniment. Also the metadata or protocols is important to distribute music information.

REFERENCES:

*Music information retrieval. (2009, June 2). In Wikipedia, The Free Encyclopedia. Retrieved 21:04, June 2, 2009, from http://en.wikipedia.org/w/index.php?title=Music_information_retrieval&oldid=294025830

*The international Society for Music Information Retrieval. Retrieved 21:10, June 2, 2009, from http://www.ismir.net/

* Downie, J. Stephen. “The Music Information Retrieval Evaluation eXchange (MIREX)”. In D-Lib magazine. Retrieved 21:23, June 2, 2009, from http://www.dlib.org/dlib/december06/downie/12downie.html

Machine Translation

In 1629 the philosopher Descartes proposed a universal language where equivalent ideas in different tongues had one symbol and the idea of using digital computers for it was proposed in 1946. And it was a fact in the 1950s, when the Georgetown Experiment (1954) involved automatic translation of Russian sentences into English. But the real progress was much slower.

The machine translation investigates the use of software to translate from one natural language to another. In a simple it works doing substitutions of words. As we can read in wikipedia the translation process may be stated as “decoding the meaning of he source text and re-encoding this meaning in the target language”.

Some translation webs: Open Trad. http://www.opentrad.org/ , Instituto Cervantes http://oesi.cervantes.es/traduccionAutomatica.html, Lucy http://www.translendium.net:8080/home/, Google http://translate.google.com/ …

REFERENCES:

* Machine Translation. (2009, March 08). In Wikipedia, The Free Encyclopedia. Retrieved 21:20, May 29, 2009, from http://en.wikipedia.org/wiki/Machine_translation

* Machine Translation, Capabilities and Limitations. By Ana Fernández Guerra. In Google, Búsqueda de Libros. Retrieved 21:30, May 29, 2009, from http://books.google.es/books?d=7TE3avRZiSoC&pg=PA153&lpg=PA153&dq=machine+translation&source=bl&ots=nhlsFcnMGP&sig=Aytcz3-lJQ2VYeuhVdg2fBDHpyY&hl=es&ei=LQzOSaD3J-TTjAfQkrTbCQ&sa=X&oi=book_result&resnum=6&ct=result#PPP1,M1

* Machine Translation, Description. In Springer, Artificial Intelligence. Retrieved 21:35, May 29, 2009, from http://www.springer.com/computer/artificial/journal/10590

German Research Centre for Artificial Intelligence

Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) is one of the world’s largest research centres of innovative software technology based on Artificial Intelligence (AI) methods. DFKI was founded in 1988. The directors are Prof. Wolfgang Wahlster and Dr. Walter G. Olthoff.

Their mission as we can read in the home page of the research centre “is the improvement of language technology through novel computational techniques for processing text, speech and knowledge, a deeper understanding of human language and thought, studying the true needs of the end user and the demands of the market”. They develop three areas: information and knowledge management, document production and natural communication

This place conducts contract research in virtually modern AI, including image and pattern recognition, knowledge management or intelligent visualization and simulation and so much more fields.The centre led a national project with the aim to translate spontaneous speech bidirectionally for German/English and German/Japanese. Currently, there are more than 90 ongoing projects at the research center. Some companies like Microsoft, BMW or SAP figures in the centre.

References:

* German Research Centre for Artificial Intelligence. (2009, June 4). In Wikipedia, The Free Encyclopedia. Retrieved 21:19, May 29, 2009, from http://en.wikipedia.org/w/index.php?title=German_Research_Centre_for_Artificial_Intelligence&oldid=294439418

* German Research Centre for Artificial Intelligence. (2009). Retrieved 12:55, March 16, 2009, from http://www.dfki.de/lt/

Question Answering Systems

One of the first Artificial Intelligence (AI) systems were Question Answer systems (QA) called BASEBALL and LUNAR, both developed in the 1960s. These systems were very effective. LUNAR, for example, was demonstrated in 1971 that it was able to answer 90% of the questions. But two of the most famous early systems were SHRDLU, which simulated in s toy world the operation of a robot, and ELIZA, which simulated a conversation with a psychologist. The 1970s and 1980s the development came with the Computational linguistic, which were projects in text comprehension and question answering. An example of it was the Unix Consultant (UC). With the expansion Word Wide Web there was, and there is, an integration of question answering with web search, like Ask.com or Google.

A Question Answering  system (QA) that answers automatically question posed in natural language. The QA computer program finds the answer to a question in a pre-structured database or a collection of natural language documents. The system answers to question types including definition, list, fact, why, how, semantically constrained, hypothetical and cross-lingual questions.

One of the problems of the QA is the word sense disambiguation that lies to the software programs that have been designed to interpret language. The main thing is that the ambiguous words or sentences can be understood in multiple ways, not only one meaning is intended. This area for the programmers is extremely challenging because they have to designe interfaces to link the gap between spoken and written language.

 

REFERENCES:

*NSIR, Question Answering System. (2002). Retrieved 20:53, May 29, 2009, from http://tangra.si.umich.edu/clair/NSIR/html/nsir.cgi 
*Powerset (company). In Powerset. Retrieved 20:34, May 29, 2009, from http://www.powerset.com/explore/semhtml/Powerset_%28company%29
*What is Word Sense Disambiguation?.By R. Kayne. In wiseGEEK, clear answers for common questions. Retrieved 20:40, May 29, 2009, from http://www.wisegeek.com/what-is-word-sense-disambiguation.htm
*AnswerBus, Question Answering System. (2001-2008). Retrieved 20:43, May 29, 2009, from http://www.answerbus.com/index.shtml

*Question Answering. (2009, March 20). In Wikipedia, The Free Encyclopedia. Retrieved 20:59, May 29, 2009, from http://en.wikipedia.org/wiki/Question_answering

The 10 topics

Those are the topics that I found them the most interesting:

1.Machine Translation

2.Spell checking

3.Word-Sense Disambiguation

4.Information Extraction

5.Emotion recognition

6.Annotation Science

7.Phonology/Morphology, POS Tagging, Word Segmentation

8.Multimodal Representations and Processing

9.Discourse and Pragmatics

10. Music information retrieval

REFERENCES

*Association for Computational Linguistics 2009. (2008-2009). In ACL-IJCNLP 2009. Retrieved 20:54, March 25, 2009, from http://www.acl-ijcnlp-2009.org/index.html

*Human Language Technologies (HLT). In Language Technology World. Retrieved 21:21, March 25, 2009, from http://www.lt-world.org/

*Association for Computational Linguistics 2008. (2007). In ACL-08: HLT. Retrieved 21:13, March 25, 2009, from http://www.ling.ohio-state.edu/acl08/index.html

*Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics 2007. (2007, January 18). In NAACL-HLT 2007. Retrieved 21:04, March 25, 2009, from http://www.cs.rochester.edu/meetings/hlt-naacl07/call_papers.shtml

*Human Language Technologies (HLT). From Deutsches Forschungszentrum für Künstliche Intelligenz. Retrieved March 25, 2009 21.35 from http://www.dfki.de/lt/projects.php

Language Techlonogy Reserch Centres

The Icelandic Centre for Language Technology

The Icelandic Centre for Language Technology (ICLT) was founded in 2005. The idea of the centre came from the LangTec Project of the Ministry of Education, Science, and Culture. ICLT is run by the Institute of Linguistics at the University of Iceland, the School of Computer Science at Reykjavik University and the Department of Lexicography at the Árni Magnússon Institute for Icelandic Studies.

ICLT works as a platform for cooperation between the participating institutions and it’s role by serving as an information centre on Icelandic Language Technology (LT) ( website: http://www.tungutaekni.is) . And it has the cooperation between universities, institutions and private companies and also organising and coordinating university education in Europe and international universities taking part in research projects in LT.

During the last few years has been carried out the research in the following areas:  Basic Language Research Kit development, context-sensitive spell checking, corpus annotation, morphology, part-of-speech tagging, shallow parsing, and speech recognition.

National Centre for Language Technology ( Dublin City University, Ireland)

The National Centre for Language Technology is in The School of Computing in Dublin City University. The Centre has five different research groups: Machine Translation, The Gram Lab, Computer Aided Language Learning, LORG and Sentiment Analysis.

These groups works in a basic and applied research in the areas of machine translation, natural language parsing, grammar induction, question answering, sentiment analysis, computer-aided language learning, software localisation, speech recognition and speech synthesis.

Its researchers are from the School of Computing, of Applied Languages and Intercultural Studies and of Electronic Engineering.

References:

*The Icelandic Centre for Language Technology. (May 2006–August 2007). Retrieved 12:46, March 16, 2009, from http://nlp.ru.is/pdf/Report06-07.pdf

* National Centre for Language Technology. (24th October 2008). Ireland. Retrieved 12:50, March 16, 2009, from http://www.nclt.dcu.ie/contact.html

Yorick Wilks and Martin Kay

YORICK WILKS

Yorick Wilks is a scientist who is Professor of Artificial Intelligence at the University of Sheffield and also a Senior Research Fellow at the Oxford Internet Institute. He was educated at Torquay Boys’ Grammar School and Cambridge. Wilks obtained his PhD in 1968.

In the early 1970s Wilks built the first operational machine translation system based on meaning structures. This was based on the one of the earliest computational work about the Preference Semantics in the field of word sense disambiguation.

Yorick Wilks has received along his career prestigious awards like Loebner Prize (in 1997),The biennial Antonio Zampolli Prize (in 2008), The Lifetime Achievement Award (in 2008) or The British Computer Society’s Lovelace Medal (in 2009).

MARTIN KAY

Martin Kay is a computer scientist specialist in computational linguistics. And as Yorick Wilks did, he studied in Cambridge. He started to work at the Cambridge Language Research Unit that was at those time one of the earliest centers for research Computational Linguistics.

He developed the chart parsing and functional unification grammar and he has contribuited in the application of finite state automata in computational phonology and morphology. He has been also regarded as a leading authority on machine translation.

References:

* Yorick Wilks. (2009). Personal Homepage. Retrieved 12:40, March 2, 2009, from http://www.dcs.shef.ac.uk/~yorick/

* Yorick Wilks. (2009, January 4). In Wikipedia, The Free Encyclopedia. Retrieved 11:33, March 2, 2009, from http://en.wikipedia.org/w/index.php?title=Yorick_Wilks&oldid=261889889

*Martin Kay. (2008, June 7). In Wikipedia, The Free Encyclopedia. Retrieved 11:32, March 2, 2009, from http://en.wikipedia.org/w/index.php?title=Martin_Kay&oldid=217746063

* Martin Kay. (2009). Personal Homepage at Stanford University. Retrieved 12:35, March 2, 2009, from http://www-linguistics.stanford.edu/people/pages/kay.shtml

ORALIDAD Y LITERATURA

     La oralidad es la forma de comunicación verbal. Se divide en dos partes: la oralidad primaria y la oralidad secundaria. La oralidad primaria se centra mas en la cultura hablada y la secundaria en la oralidad trabajada, que despues es posible que sea escrita. Consta de cinco factores: el lingüistico, el extralingüistico, el discursivo,  el socio lingüistico y el factor social. El primer factor, el lingüistico, bien utilizado contruyen frases correctas. El extralingüistico trata mas el ritmo, entonación y aspectos del habla. La estructura del habla, narrativo, expositivo… pertenecería al factor discursivo. El factor socio lingüistico hace referencia al efecto que tiene la sociedad del orador a  la hora de escojer las palabras. Y, por último, la comprensión de la oralidad pertenece al factor social.

     En cambio la literatura sería lo escrito. De hecho, literatura significa lo que está escrito. Con está acepción podemos decir que está mal empleado usar “literatura oral”. Porque algo oral no es algo escrito. Por ello siempre se ha intentado buscar una palabra para denominar a la cultura transmitida oralmente, pero siempre se encuentra alguna incorrección.

REFERENCIAS:

*Walter J. Ong. On Wikipedia the free encyclopedia. Retrieved February 7 15:40. http://en.wikipedia.org/wiki/Walter_J._Ong

*Orality, Literacy, Digitality. Retrieved February 7 16:00. http://www.tarleton.edu/~lilly/discuss2.htm

*Oralidad y escritura. Retrieved February 7 16:05. http://www.monografias.com/trabajos6/ores/ores.shtml

*Review of Walter J. Ong’s Orality and Literacy. Retrieved February 7 16:08.  http://www.engl.niu.edu/wac/ong_rvw.html