CiteULike

Citeulike is an free online web where anyone can write different links about academic information. It has been on the web since 2004 and designed specifically for the needs of scientists and scholars. It’s main aim is  to promote and to develop the sharing of scientific references amongst researchers.

We use citeulike to cite different articles, web pages or even photos and small scripts read the citation information from the web page and import into the CiteULike database for the currently logged in user. The most important tags are sited in the front page and these can be clicked on in order to see the information. This is called importing information. However, you can also export information from your own account just by adding entries and making them public for everyone. The page also gives the oportunity to join to different groups that are usually labs, institutions, professions, or research areas. Groups allow individual users to collaborate with other users to build a library of references.

The only thing one has to do is register to have their own account and start publishing things.

REFERENCES:

* CiteULike. (2009, November 29). In Wikipedia, The Free Encyclopedia. Retrieved 12:36, January 4, 2010, from http://en.wikipedia.org/w/index.php?title=CiteULike&oldid=328621862

Markup language

Markup language is a way of text annotating and it is written in a different way from the text in itself. One of the most known way of markeup language is the Hypertext Markup Language or in other words, HTML  this is one of the document formats in the world wide web, as well as another form of the SGML.

The term markup language comes from the “marking up” which means to add notes on one side of what has already been written.  Apart from this type of markup language there are other types: GenCode, TeX, Scribe, GML, SGML, XML and XHTML.
    •Darwin Information Typing Architecture (DITA)
    •DocBook
    •Extensible HyperText Markup Language (XHTML)
    •Extensible Markup Language (XML)
    •Generalized Markup Language (GML)
    •HyperText Markup Language (HTML)

The most common feature of the markedup language is that they intermix the text with the instructions. There is no necessity at all for doing this as they can be splited up by using pointers, offsets, IDs, or other methods to do so.

The information attached   <like this>  are markedup instructions and those with codes like  h1, p, and em  are examples.

 

References:

*Markup language. (2009, November 2). In Wikipedia, The Free Encyclopedia. Retrieved 15:46, November 15, 2009, from http://en.wikipedia.org/w/index.php?title=Markup_language&oldid=323542392

*Definición de lenguje de marcas. (2009) In the webpage of Diccionario Informático. Retrieved, November 15, 2009 from http://www.alegsa.com.ar/Dic/lenguaje%20de%20marcas.php

Digital libraries

Since technology has developed, we can find different kind of web pages. Some of them are digital libraries, in other words, web pages where different collections are stored in digital formats. This means that these kind of libraries are accessible by computer for everybody having internet conexion.

Although some of the digital libraries are even older than the web such as Project Perseus, Project Gutenberg, and ibiblio, there are more and more new libraries on the net. For instance, two of the many english digital libraries are The British Library,and the Darlingtong Digital Library.  This last library was created from the first collections of books, manuscripts, atlases, and maps donated to the University of Pittsburgh by William M. Darlington. Altgough he started acquiring books on subjects such as Western Pennsylvania and the Ohio Valley, later he would expand these topics associating them the exploration of the Trans-Mississippi, the Far West, and even world history. Little y little he also started including atlases and maps, broadsides, manuscripts, lithographs, and works of art,  John James Audubon’s Birds of America for instance.

 When William Darlington died, his family continued what he started, his daughters as well as his wife. This way what at the beginning was just his, became a family thing, a family collection of works, being his daughter Edith and wife Mary who first made a donation to the University in 1918.

Like this, nowadays the library is runned by three librarians, three scanning technicians, and several University of Pittsburgh graduate students enrolled in the School of Information Science. As mentioned before, the content of the library is based on atlases, books, broadsides, images, manuscripts and finally, maps. These are the main categories in which it is divided.

 

REFERENCES:

*Digital library. (2009, October 14). In Wikipedia, The Free Encyclopedia. Retrieved 18:15, October 20, 2009, from http://en.wikipedia.org/w/index.php?title=Digital_library&oldid=319815245

*The Darlington Digital library (2007). In the web page of the Darlington Digital Library. Retrieved, October 20, 2009 from http://digital.library.pitt.edu/d/darlington/index.html

*List of digital library projects. (2009, September 17). In Wikipedia, The Free Encyclopedia. Retrieved 18:19, October 20, 2009, from http://en.wikipedia.org/w/index.php?title=List_of_digital_library_projects&oldid=314516494

*British Library. (2009, October 9). In Wikipedia, The Free Encyclopedia. Retrieved 18:19, October 20, 2009, from http://en.wikipedia.org/w/index.php?title=British_Library&oldid=318866413

Ebooks and paper books.

An ebook is what we now know as an electronical version of the traditional book. It can either refer to the book or to the deviced used to do so.

Although there are many electronical deviced that can be used as ebooks, it was in the XXI century when a device just for electronical books appered. With these tools, they tried to emulate the traditional paper book. They wanted these to be even more easy to use. Sometimes using them an ebook in daylight can be impossible. However, through many researches they achieved to make it better so that it could be read while in the street.

There are many differences between the traditional books and the ebooks:

* The printed book has no limits as the screen on ebooks has.

* A printed book can be brought from one place to another without the necessity of cables to load the battery.

*A printed book cannot be adpted in order to be easier to the reader. However, the ebook offer the opportunity to adapt the text so that the reading can  be easier.

*A printed book does not offer hypertextiality while the electronic book does.

Among these differences, as usual there are some advantanges on each type of book:

Ebook:

• Everybody can download them in a few minutes
• They can be printed as well
• They are less expensive

Printed books:

• They have an easier format exclusively for reading
• Portability, easier for the eyes of the reader, easier to handle
• Those without computer knowledge can use them

REFERENCES:

*E-book. (2009, October 11). In Wikipedia, The Free Encyclopedia. Retrieved 15:39, October 11, 2009, from http://en.wikipedia.org/w/index.php?title=E-book&oldid=319241552

*The Difference Between Our Print Books And E-Books (2005). In Multidiet. Retrieved, October 11, 2009, from http://www.multidiet.com/adm/p-vs-ebook.htm

* Panorama de la edicion digital (2003). In Slideshare. Retrieved, October 11, 2009, from http://www.slideshare.net/JosebaAbaitua/panorama-de-la-edicin-digital

Tim Berners-Lee

 

 

Sir Timothy John Berners-Lee, was born on the 8th june 1955 in London, UK. He was graduated in phisycs from the university of Oxford in the year 1976. We can say that he was one of the inventors of the World Wide Web as he first  made the proposal for it in March 1989. However, the first general ideas were outlined before Tim Berners-Lee ‘s proposal. He became sir in 2004 as was knighted by Queen Elizabeth.

He is the director of the world wide web consortium, which developes the technologies to lead the web to its full potential. He is the  founder of the world wide web foundation as well, and a senior researcher and a holder of the 3comb Founders Chair at the MIT Computer Science and Artificial Intelligence Laboratory. While working at CERN, he proposed a project based on the hypertext, to make easier the sharing and updating information among all the reasearchers.

In 1989 he realized that internet was one of the biggests inventions in the world, so he decided that it would be a good idea if the hypertext and the internet became one. That’s why he proposed to do so, giving birth to the World Wide Web, the most used device or computer application in the world nowadays. His first proposal of the web wasn’t so well known or recognized,so with the help of Robert Cailliau they achieved to make a better version of it which was accepted by their manager.

In 1994 he joined the scientifict laboratory of the computation and artificial intelligence of Massachusetts Institute of Technology, that’s why he moved to the EE. UU were he started the W3C which is currently under his direction.

In 2001 he became part of the Royal Society and he was awarded several time with different kind of prices such as the Japan Prize, the Prince of Asturias Foundation Prize, the Millennium Technology Prize and Germany’s Die Quadriga award.

He is the author of “Waving the web”.

References:

*Tim Berners-Lee. (2009, October 5). In Wikipedia, The Free Encyclopedia. Retrieved 16:51, October 5, 2009, from http://en.wikipedia.org/w/index.php?title=Tim_Berners-Lee&oldid=318071668

*  Tim Berners-Lee. (2009). Retrieved, October 5, 2009, from http://www.w3.org/People/Berners-Lee/.

Machine Translation (Q3)

Machine translation, also known as MT, is nowadays one of the most successful devices in the world wide web. As its name claims, it has to do with translating either texts or single sentences. We can say that machine translation is a subfield of computational linguistics which looks into  computer software, in order to translate texts or sentences from one natural language to another. Even if at the beginning there were not that much tranlation machines, nowadays there are  significantly much more, which are considerably better that they used to be.

Old translation tools are now being replaced by mordern ones which are surely boosting their performance, however, they must be used with caution as they will not be so useful for every aspect of our daily lives.

As I already have claimed, machine translators translate texts from one natural language to another with little difficulties. Nevertheless, there are some sentences or texts which require a different process of translation. Most of them need to be translated by using corpus techniques that allow  better dealing with of differences in linguistic typology, phrase recognition, and translation of idioms, as well as the isolation of anomalies. When translating, the computer may have some other difficulties such as the double meaning of some words, for this word sense disambiguation worries to fins a suitable word for the text. One which does not mean something else. The way that the computer distinguishes the different meanings of the words is by a “universal encyclopedia”, due to this encyclopedia the machine is able to devide meaning approaches this way:

* shallow approaches: This doesn’t need the knowledge required to understand the text, this would be what we call simple translation.

*Deep approaches: They need to use the “universal encyclopedia” these kind of translation needs to translate difficult words, especific word for some specific topics.

 

References:

*Machine translation. (2009, May 27). In Wikipedia, The Free Encyclopedia. Retrieved 13:19, May 27, 2009, from http://en.wikipedia.org/w/index.php?title=Machine_translation&oldid=292599882

* Machine translation. (2009,May 27). In AITopics. Retrieved May 27, 2009, from http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/MachineTranslation

Outlines to write an article (Q4)

Wikipedia is an online encyclopedia where anyone can find different kind of information about different things such as biographies, information about different devices.

While writing an article in this encyclopedia, there are several rules about to follow. First of all, those who want to write an article have to follow an outline which devides each part that the article is supposed to have. To do this, you can devide the article in different parts such as life if it is a biography, work, overview, uses, see also, external links and references.

As a student of  human language technologies, I have been given some examples of these outlines to follow. So, looking at these four examples, I may say that the second one (http://en.wikipedia.org/w/index.php?title=Pattern_recognition&oldid=281282782) is the one that I consider the best. The simplicity of it is what lead me to think that way, an outline for an article can’t be unclear and confusing, so, having a long planning can lead people to misunderstandings and mixing up information.

Another thing to point out is the length given to the writing, the result of a long planning will be a long article which is not the first aim of the article. It has to be entertaining but brief, it may not exceed the 300 words more or less, that’s why the more simple the outline is the most interesting the writing will be.

References:

*Pattern recognition. (2009, April 2). In Wikipedia, The Free Encyclopedia. Retrieved 10:15, May 25, 2009, from http://en.wikipedia.org/w/index.php?title=Pattern_recognition&oldid=281282782

Three research topics (Q2)

 -Question answering:

This device gives us the chance to obtain an immediate answer to a question  posed in natural language, in the web.  To answer the question the computer may use information either from a data base or a text collection written in natural laguage.

A question answering program can deal with many questions as long as they contain: fact, list, definition, How, Why, hypothetis… The information to answer the question could be retrieved from different types of colletions in the world wide web, local document collections, internal organization documents, newswire reports for instance.

*Closed-domain question answering deals with questions from specific topics.

*Open-domain question answering deals with questions in general, those wich are answered by a general knowledge.

There are two differt methods of answering: shallow and deep. The shallow answering consists of asking simple questions such as what is X? and the computer will look for the X in order to give a suitable definition or answer to the question. However, in the deep case whatever the question is like, the computer will answer it without any problems. Even if it is a difficult and a specific question with difficult syntactic structures, it will be able to analyse, understand and answer it.

So, it is known that this device is now pretty useful to know the answer to any question that cannot be answered without using the internet.

-Natural Language Processing 

Natural Language Processing is also known as Humal Language Technology, and it is just a field in computer science which deals with the interaction between humans or humans and computers. This consist on rewriting the information on the databases is a understandably way, so that information on them can be readable for people. While using the natural language for humans is such an easy task, for computers it is kind of impossible to put everything in a way that people can understand.

To sum up, the NLP is a subfield which deals with interaction between humans an humans and computers.

–Speech recognition:

Speech recognition is a device used to convert spoken words into written ones. It is alsocommomly known as automatic speech recognition or computer speech recognition. It is also sometimes called voice recognition, however, this is not such a good term since it is actually referring to speaker recognition than speech recognition. This includes some applications such as voice dialing, call routing (to make collect calls), domotic appliance control and content-based spoken audio search (find a posdcast by saying some particular words), simple data entry, preparation of structured documents, speech-to-text processing, and in aircraft cockpits.

There are some domains that nowadays obtain a considerable benefit from this speech recgnition: the health care domain, the military domain, telephones and other domains and finally disabled people.

REFERENCES:

Speech recognition. (2009, March 25). In Wikipedia, The Free Encyclopedia. Retrieved, May 5, 2009, from http://en.wikipedia.org/w/index.php?title=Speech_recognition&oldid=279564386

Question answering. (2009, March 20). In Wikipedia, The Free Encyclopedia. Retrieved, May 5, 2009, from http://en.wikipedia.org/w/index.php?title=Question_answering&oldid=278611780

Natural Language Processing.( 2009). In the microsoft research blog. Retrieved, May 5, 2009, from http://research.microsoft.com/en-us/groups/nlp/

Natural language processing. (2009, June 11). In Wikipedia, The Free Encyclopedia. Retrieved 08:27, June 11, 2009, from http://en.wikipedia.org/w/index.php?title=Natural_language_processing&oldid=295743075

List of the main research topics (Q2)

Every research centre may have its own research topics, however, there are many topics discussed in every centre. Many of these research centres, doesn’t matter wether they are from overseas or even from the country, are going through these main topics in order to show us the same information in different ways.

Some of these research centres can be:

•  German Research Centre for Artificial Intelligence.
• Advanced Institute for Information & Communication Technology. Meraka Institute.
• National Centre for Language Technology. Dublin City University.
• Language Technologies Research Centre. International Institute of Information Technology.

Well, once we know some of these research centres, some of the main research topics concerning the researchers are:

• Latent semantic analisys.
• Natural language generation.
• Speech recognition.
• Data mining.
• Natural language processing.
• Computational semantics.
• Text mining.
• Question answering.

REFERENCES:

Language Technology World. (2007, February 19). Retrieved April22, 2009, from http://www.lt-world.org/

Latent semantic analysis (2008). In Scholarpedia. Retrieved, April22, 2009 from http://www.scholarpedia.org/article/Latent_semantic_analysis

Natural Language Generation(Fri Feb 26,1999). In Gred Herzog’s web site. Retrieved, Aperil22, 2009 from http://www.dfki.de/fluids/Natural_Language_Generation.html

Text mining. (2009, March 25). In Wikipedia, The Free Encyclopedia. Retrieved,April22, 2009, from http://en.wikipedia.org/w/index.php?title=Text_mining&oldid=279549834

Natural language processing. (2009, March 25). In Wikipedia, The Free Encyclopedia. Retrieved April22, 2009, from http://en.wikipedia.org/w/index.php?title=Natural_language_processing&oldid=279571047

Speech recognition. (2009, March 25). In Wikipedia, The Free Encyclopedia. Retrieved April22, 2009, from http://en.wikipedia.org/w/index.php?title=Speech_recognition&oldid=279564386

About computational semantics(17 June 2005). In Patrick Blackburn’s page. Retrieved, April22, 2009, from http://www.loria.fr/~blackbur/aboutComSem.html

Question answering. (2009, March 20). In Wikipedia, The Free Encyclopedia. Retrieved April22, 2009, from http://en.wikipedia.org/w/index.php?title=Question_answering&oldid=278611780

Data mining. (2009, March 29). In Wikipedia, The Free Encyclopedia. Retrieved April22, 2009, from http://en.wikipedia.org/w/index.php?title=Data_mining&oldid=280435850

Human Language Technologies (researchers) (Q1)

As in every research, there are some researcher whose names are really well known among the research centres. Here we have two of them:

Martin Kay, a British researcher specially known for working in Computational linguistics. The first step he made as a researchers has been in the Cambridge Language Research Unity in 1958. Then he moved to the Rand corporation where he became head of linguistics and machine translations. He left this corporation behind in 1972 and joined the Department of Computer Science. In he joined the Stanford university as a half-time professor in linguistics, where he is currently working . Apart from this he is also a Honorary Professor of Computational Linguistics at Saarland University.

Among his achievements we can find the developement of chart parsing and functional unification grammar as well as contributions to the application of finite state automata in the fields of  computational phonology and morphology. He has also been regarded as a leading authorithy of machine translation.

Some of Martin Kay’s publications are:

• Martin Kay: A life of Language. Computational Linguistics 31(4): 425-438 (2005)
• Martin Kay: The Proper Place of Men and Machines in Language Translation. Machine Translation 12(1-2): 3-23 (1997)
• Martin Kay: Chart Generation. ACL 1996: 200-204
  Mark Johnson, Martin Kay: Parsing and Empty Nodes. Computational Linguistics 20(2): 289-300 (1994)
• Ronald M. Kaplan, Martin Kay: Regular Models of Phonological Rule Systems. Computational Linguistics 20(3): 331-378 (1994)
• Martin Kay: Nonconcatenative Finite-State Morphology. EACL 1987: 2-10(1987)
• Martin Kay: Unification in Grammar. Natural Language Understanding and Natural Language Understanding Workshop (1984): 233-240

Yorick Wilks, a British Computer Scientist and at the same time, Professor of Artificial Intelligence at the University of Sheffield, as well as a Senior Research Fellow of the Oxford Internet Institute.

His professional life is quite wide but we can mention that he has been Head of Department of Computer Science (University of Sheffield, 1998 – 2002), Director at Computing Research Laboratory and Professor of Computer Science (New Mexico State University, 1985 – 1993), Professor of Computer Science (University of Essex, 1984 – 1985), Head of Department of Linguistics (University of Essex, 1980 – 1983), Professor of Linguistics (1978 – 1983), Research Associate and Lecturer at Artificial Intelligence Laboratory (Stanford University, California, 1970 – 1974)…

Some of his publications are:

• Yorick Wilks: What would a Wittgensteinian computational linguistics be like. AISB Convention 2008: 1-6
• Yorick Wilks: On Whose Shoulders? Computational Linguistics 34(4): 471-486 (2008)
• Yorick Wilks: The Semantic Web: Apotheosis of Annotation, but What Are Its Semantics? IEEE Intelligent Systems 23(3): 41-49 (2008)
• Yorick Wilks: Karen Spärck Jones (1935-2007). IEEE Intelligent Systems 22(3): 8-9 (2007)
• Yorick Wilks: Getting Meaning into the Machine. IEEE Intelligent Systems 21(3): 70-71 (2006)
• Yorick Wilks: IR and AI: Traditions of Representation and Anti-representation in Information Processing. ECIR 2004: 12-26
• Yorick Wilks: Artificial Companions. MLMI 2004: 36-45
• Yorick Wilks, Roberta Catizone: Can We Make Information Extraction More Adaptive? SCIE 1999: 1-16
• Dan Fass, Yorick Wilks: Preference Semantics, III-Formedness, and Metaphor. American Journal of Computational Linguistics 9(3-4): 178-187 (1983)
• Yorick Wilks: Language, Vision and Metaphor. Artif. Intell. Rev. 9(4-5): 273-289 (1995)

REFERENCES:

• Yorick Wilks. (2009, January 4). In Wikipedia, The Free Encyclopedia. Retrieved 19:52, March 15, 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 19:52, March 15, 2009, from http://en.wikipedia.org/w/index.php?title=Martin_Kay&oldid=217746063
• Yorick Wilks(2008), Yorick Wilks’ web page. Retrieved 20.54, March15 ,2009, from http://www.dcs.shef.ac.uk/~yorick/
• Martin Kay’s publications (Mar 13 16:28:13 2009). Universität trier. Retrieved 20.54 March 15,2009, from, http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/k/Kay:Martin.html
• Yorick Wilk’s publications (Mar 13 16:28:13 2009). Universität trier. Retrieved 20.55 March 15,2009, from, http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/w/Wilks:Yorick.html