Documentación digital vs. Documentación tradicional

En el siguiente artículo abordaré las diferencias existentes entre lo que hoy en dia se conoce como documentación digital y la llamada documentación tradicional.

Empezemos pues por la Documentación digital:

Se habla de documentos digitales cuando” la información, no importa su forma: texto, sonido, imagen fija o en movimiento, se registra en un medio electrónico a través de codificaciones que se basan en el uso de combinaciones de señales eléctricas positivas y negativas, las cuales se representan por medio de los dígitos “0” ,señal negativa, y “1” ,señal positiva”. Es decir, cuando un documento  no recibe su nombre de la forma mediática en que se presenta la información, sino directamente de la manera particular en la que se registra.

Pueden ser:

• Impresos digitalizados, aquellos que son el resultado de escanear un documento impreso.
• Digitales para imprimir: elaborados directamente por medio de programas como “Word”.
• Digitales multimediáticos, pensados para ser consultados en un ordenador y beneficiarse de  posibilidades electrónicas comoel hipertexto.

Sin embargo, es necesario también tener en cuenta :

• Computabilidad: cuando la información puede ser procesada vía ordenador.
• Virtualidad: cuando la información digital no está sujeta a las limitaciones propias de la analógica.
• Capacidad.

Por otro lado y en cuanto a Documentos Tradicionales debemos mencionar que no disponen de las mismas características que los anteriores: se refieren a cualquier unidad de información registrada en un soporte que permita su almacenamiento y su posterior recuperación, y por tanto, que permita también una consulta ilimitada. Al  ofrecer solo texto e imagen estática, ni sonido ni imagen animada, la documentación traicional presenta una gran cantidad de limitaciones si realizamos una breve comparación con el tipo de documentación  mencionado anteriormente.

Por último y para finalizar y teniendo en cuenta todo lo mencionado anteriormente, podemos anotar las siguientes conclusiones:

1. La documentación digital presenta  varias ventajas con respecto a la documentación tradicional como:

-Disponer de información multimedia: imagenes, textos y sonido. Lo que se conoce como “carácter multimediático del documento digital“

-Hacer  posible la recuperación de la información perdida en un momento determinado.

-Ser virtual y  tener facilidad para ser reproducido, transmitido y almacenado.

-Interactividad, es decir, permitir al lector preguntar por contenidos, comentarios… ofreciendo incluso la posibilidad de modificar de forma sencilla el formato del texto.

-Propiciar  una relación muy directa con los usuarios de este tipo de edición.

-Hacer posible que la cantidad de información por unidad de volumen sea “infinita”.

-La hipertextualidad, permitiendo así al lector la libre navegación por el documento y rompiendo con el ordén natural que debería ser seguido en un texto tradicional.

-La mutabilidad y volatilidad:  haciendo igual de sencilla la posibilidad tanto de hacer desaparecer un texto cualquiera como el efectuar cambios en el sin la necesidad de tener que reproducirlo entero de nuevo. 

       2. Sin embargo, no todo son ventajas en la edición digital, ya que:

-Es necesario el poseer un ordenador personal mediante el que realizar la edición de documentos.

-La existencia de aparatos de lectura considerable es aún muy reducida.

-Además, como el medio de trabajo es informatizado esto conlleva a posibles problemas con la red u otros de caracter técnico.

-Poca ergonomía.

Para finalizar, hay señalar dos de las ventajas más notables de la edición tradicional: la confortabilidad y lo practico del formato ya que siempre se puede llevar y leer en cualquier lugar.

 

FUENTES DE INFORMACION EXTRAIDAS DE:

-http://www.msinfo.info/propuestas/documentos/documentos_digitales.html

-http://www.elprofesionaldelainformacion.com/contenidos/2001/diciembre/5.pdf

Social Bookmarking

Social bookmarking is a  new technology that has changed the way you surf on the Net.It  allows you to store links to websites or blogs, classify, evaluate, and to make a comment on them or find them from any computer.

When you keep an item on the server of one of these portals: Digg, Yigg, del.icio.us, My Yahoo or Google, you can make it available to other Internet users or to add your comment. Instead of using the search engine, you find the information through the members of the community of users (Usercomunnity) of a social bookmarking page. In that way, the network browsing experience becomes a “social” one because it is not the search engine algorithm that determines the importance of web content, but the number of system users that can access to it.

Visit the websites mentioned in the previous lines and find out more information about their social bookmarking service. Each portal has its own design to meet the needs of Web search.

However when we are using this type of system based on “tags”, we can find also some disadvantages, like:

 - There is no pre-established system of keywords or categories

 - There is no predefined structure for the tags (eg plural / singular, upper, accents, etc.). This can create spelling mistakes.

-  Tags can have more than one meaning and provide inaccurate results due to confusion between synonyms or antonyms.

- There is no way of structuring the ‘tags’ in a hierarchical way

SOURCES:

• http://www.swissinfo.ch/spa/swissinfo.html?siteSect=46&sid=7991124
• http://es.wikipedia.org/wiki/Marcadores_sociales

Some Characteristics of Translation (Q3)

The main characteristics of the translation task according with a FEMTI´S report are the following :

• Assimilation: The ultimate purpose of the assimilation task (of which translation forms a part) is to monitor a (relatively) large volume of texts produced by people outside the organization, in (usually) several languages.

 

• Document routing or sorting: The purpose of document routing / sorting is to scan incoming translated documents quickly in order to send them to the appropriate points for further processing or storage.

 

• Information extraction or summarization: The purpose of information extraction or summarization is to extract some portion(s) of the translated text, either manually or automatically, for subsequent processing or storage. Information extraction is typically concerned with filling templates by identifying atomic elements of events. In contrast, summarization aims to provide a self-contained and internally cohesive text which serves as a selective account of the original.

 

SOURCES:

• http://www.issco.unige.ch:8080/cocoon/femti/st-home.html

Concepts related with Translation

In that article I am going to explain some concepts which are very closely relationated with the translation world, nad these terms are the following:

• Machine translation: MT performs simple substitution of words in one natural language for words in another. Using corpus techniques, more complex translations may be attempted, allowing for better handling of differences in linguistic typology, phrase recognition and translation of idioms, as well as the isolation of anomalies. Improved output quality can also be achieved by human intervention: for example some systems are able to translate more accurately if the user has unambiguosly identified which words in text are names . With the assitance of these techniques, MT has proven useful as a tool to assist human translators, and in some cases can even produce output that can be used “as is”.
• Computer-assisted translation: is a form of translation where in a human translator translates texts using computer software designes to support and facilitate the translation process. CAT is some times called machine-assisted, or machine-aidded translation.
• Translation technology: Translation is the action of interpretation of the meaning of a text, and a subsequent production of an equivalent text, also called a translation, that comunicates the same message in another language. The text to be translated is called the “source text” and the language it is to be translated into is called the “target language” and the final product is sometimes called the “target text”.
• Multilingual content management:A multilingual website is usually a mixture of global and local content. Local content presents no particular content management issues; global content-which has to be translated across all languages locales-does. Deciding where multiple versions of content are going to be required and where content can be maintained separately for different locales is a critical decison that will affect how a site should be maintained and what it will cost.

SOURCES:

• http://www.wikipedia.org/

Explanation of Some of The Topics: Machine Learning (Q2)

As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to “learn”. At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.

The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related not only to data mining and statistics, but also theoretical computer science.

• Applications

Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.

• Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method.

Some statistical machine learning researchers create methods within the framework of Bayesian statistics.

SOURCES:

• http://en.wikipedia.org/wiki/Machine_learning

Explanation of three of Research Topics (Q2)

In that article we are going to talk about the different and the most important research topics which are relationated with the Human Language Technologies.

The German Research Center for Artificial Intelligence elaborates that themes in their research:

• Exploiting -and automatically extending-ontologies for content processing.
• tighter integration of shallow and deep techniques in processing.
• Enriching deep processing with stadistical methods.
• Combining language checking with structuring tools in document authoring.
• Document indexing for German and English.
• Automatically associating recognized information with related information and thus buiding up collective knowledge.
• Automatically structuring and visualizing extracted information.
• Processing information encoded in multiple languages, among them Chinese and Japanese

 

The Edimburgh Language Technology Group researh mainly in the following areas:

• Combining Shallow Semantics and Domain knowledge (EASIE).
• Text Mining for Biomedical Content Curation (TXM).
• Cross-retail multi-agent Retail Comparison (CROSSMARC).
• Smart Qualitative Data: Methods and Community tools for Data  Mark-up (SQUAD).
• Machine Learning for named Entity Recognition (SEER).
• Integrated Models and Tools for Fine-Grained Prosody in Discourse (Synthesis).
• Joint Action Science and Technology (JAST).
• Study of how pairs colaborate when in plannig a route on a map (Collaborating using diagrams) 

 

The Austrian Research Institute for Artificial Intelligence (OFAI) develop linguistic resources and processes as well as application prototypes:

• LINGUISTIC RESOURCES AND PROCESSES
  • Typed unification-based grammar formalisms.
  • Development of a HPSG-based grammar for German.
  • Natural Language Generation.
  • Speech Synthesis.
  • Computational morphology.
• APPLICATION PROTOTYPES
  • Natural Language interfaces and advisory systems.
  • concept-to-speech systems.

 

SOURCES:

• The German Research Center for Artificial Intelligence from  http://www.dfki.de/lt/lt-general.php
• The Edimburgh Language Technology Group from http://www.ltg.ed.ac.uk/projects
•  The Austrian Research Institute for Artificial Intelligence (OFAI) from http://www.ofai.at/research/nlu/

Hans Uszkoreit (Q1)

Uszkoreit is Professor of Computational Linguistics at Saarland University. He serves also as Scientific Director at the German Research Center for Artificial Intellingence (DFKI) where he heads the DFKI Language Technology Lab.  At the same time, by cooptation he is Professor of the Computer Science Department.

He studied Linguistics and Computer Science at The Technical University of Berlin and the University of Texas at Austin. During his time in Austin he also worked as a research associate in a large machine translation projects at the Linguistics Research Center. In 1984 he received his Ph.D in linguistics from The University of Texas. From 1982 until 1986, he worked as a computer scientist at the Artificial Intelligence Center of SRI International in Menlo Park, Ca. During this time he was also afilliated with the Center of the Study of Language and Information at Standford University as a senior researcher and later as a project leader. in 1986 he spent six months in Stuttgart to work for IBM Germany as a project leader in the project LILOG. During this time he also taught at the University of Stuttgart. 

in 1988 he was appointed to a newly created chair of Computational Linguistics at Saarland University and started the Deparment of Computational Linguistics and Phonetics. in 1989 he became the head of the newly founded Language Technology Lab. at DFKI. He has been a co-founder and principal investigator of the Special Collaborative Research Division “Resource-Adaptive Cognitive Processes” of the DFG. He is co-founder and professor of the of the “European Posgraduate Program Language Technology and Cognitive Systems”, a joint Ph.D. program with the University of Edinburgh.    

Some of his publications:

• Uszkoreit, H. (2007) Methods and Applications for Relation Detection. In: Proceedings of the Third IEEE International Conference on Natural Language Processing and Knowledge Engineering, Beijing, 2007.
• Uszkoreit, H., V. Kordoni, V. Kubon, M. Rosner and S. Kirchmeier-Andersen. (2005). ‘Language Technology from a European Perspective’. In Chris Brew and Dragomir R. Radev (eds.), Proceedings of the Second ACL Workshop on Effective Tools and Methodologies for Teaching NLP and CL, pp. 43 – 48, University of Michigan – Ann Arbor, June 2005, Association for Computational Linguistics.
• Uszkoreit, H. & B. Joerg (2003 A Virtual Information Center for Language Technology: Ontology, Datastructure, Realization, In: Nordic Language Technology Yearbook,  Museum Tusculanums Forlag, Copenhagen.

 Sources:

• HANS USZKOREIT  Short Curriculum Vitae from http://www.coli.uni-saarland.de/~hansu/bio.html

HLT´S Research Centres (Q1)

There are a lot of research Centres, europeans and internationals, which tell us more things about the Human Languages Technologies and which are available on the Net. These are some of them:

• National Centre for Language Technology (NCLT): Language is the key modality in communication. The National Centre for Language Technology conducts research into the processing of human language by computers such as speech recognition and synthesis, machine translation, human-computer interfaces, information rhltcetrieval and extraction, the teaching and learning of languages using computers and software localization and globalization. Research in Human Language Technology (HLT) is interdisciplinary and includes Natural Language Processing (NLP) and Computational Linguistics (CL). HLT has substantial economic implications and potential. The centre carries out basic research and develops applications. Director rof. Josef van Genabith. Administrator r. Yafa Al-Raheb.
• HKUST Human Language Technology Center: Is a multidisciplinary research center  at the hong Kong University of Science and Technology (HKUST) whose mission is to lead state-of-the-art research directions that drive the development of new applications in both text and spoken language technology. HLTC is led by seven faculty members from de EE and the CS departments: Oscar Au, Roland Chin, Pascale Fung, Brian Mak, Bertram Shi, Manhung Siu and Dekai Wu, specializing in speech adn signal processing, stadistical and corpus-based natural language processing, machine translation, text mining, information extraction, Chinese Language processing, knowledge management, and related fields. Special emphasis is given to machine processing of Chinese language and Chinese information. Sistems built at HLCT include automated language translation for the Internet, speech-based web browsing, and speech recognition for the telephone.
• Language Technology Group: Language Technology (LT) forms a major research area at the Austrian Research Institute for Artificial Intelligence (OFAI) since its inception in 1984. We conduct research in modeling and processing human languages, especially for German. This includes constructing linguistic resources, processing algorithms, and application prototypes. The language technology Group at OFAI is a member of the EU´s European Network of Excellence in Human Language Technologies (ELSNET).

 Sources:

• National Centre for Language Technology (NCLT), from http://www.nclt.dcu.ie/
• HKUST Human Language Technology Center from http://www.cs.ust.hk/~hltc
• Language Technology Group from  http://www.ofai.at/research/nlu/

Different definitions of Human Language Technologies (Q1)

In our today´s society the term “Human Language Technologies” is one of the most cited because of its great importance, so, there are a lot of definitions made by specialists whom we are going to write and to mention in this article.

One of the most important definitions of HLT is made by the free encyclopedia Wikipedia:

 “Human Language Technology (HLT) consists of computational linguistics (or CL) and speech technology as its core but includes also many application oriented aspects of them. Language technology is closely connected to computer science and general linguistics.”

 Another important definition of that term is given by Hans Uszkoreit, who is professor of Computational Linguistics at the Department of Computational Linguistics and Phonetics of Saarland University at Saabrücken. That scientific Director at the German Research Center for Artificial Intelligence (DFKI) and head of DFKI Language Technology Laboratory defines:

 ” Language technology — sometimes also referred to as human language technology — comprises computational methods, computer programs and electronic devices that are specialized for analyzing, producing or modifying texts and speech. These systems must be based on some knowledge of human language. Therefore language technology defines the engineering branch of computational linguistics”.

 And a third definition of Human Language Technology could be the definition given by the African advanced Institute for Information and Communication Technology, Meraka Institute who says:

 “Human Language Technology (HLT) makes it easier for people to interact with machines. This can benefit a wide range of people – from illiterate farmers in remote villages who want to obtain relevant medical information over a cell phone, to scientists in state-of-the-art laboratories who want to focus on problem-solving with computers”.  

 ·         SOURCES:

 

• o    Language technology In Wikipedia, The Free Encyclopedia from http://en.wikipedia.org/w/index.phptitle=Language_technology&oldid=179070229
• o    What is Language Technology? Language Technology Lab. from http://www.dfki.de/lt/lt-general.php.
•  o    African Advanced Institute for Information and Communication Technology from http://www.meraka.org.za/humanLanguage.htm 

 

 

 

HTML

HTML is the English acronym for HyperText Markup Language, which was translated into Spanish as a language tags Hypertext .It is a markup language designed for structuring and presenting texts in the form of hypertext, which is the standard format of Web pages. Thanks to the Internet and web browsers like Internet Explorer, Opera, Firefox, Netscape or Safari, HTML has become one of the most popular formats, and easy to learn that exist for the preparation of documents for the site.

HTML is not a programming language, although permits include code in Programming Languages, under certain criteria, expanding its capacity and functionality, but this will be achieved exceeding the scope of HTML itself.

Codes basic HTML

• <html> Sets the beginning of the HTML document, tells the browser that what follows should be interpreted as HTML code.  
• <head> Sets the header of the HTML document, this header usually contains information about the document that is not shown directly to the user. There’s the title of the browser window. Within the header <head> we find:
  •  <title> Sets the page title. Usually, the title appears in the title bar above the window.
  • <link>: To link the site to style sheets or icons. For example: <link rel=”stylesheet” href=”/style.css” type=”text/css”>
  • <style>: To place style internal page, either using CSS, JavaScript or other similar languages. It is not necessary to place if we are going to link to an external file using the label <link>
• <body>: Defining the content or main body of the document. This part of html document shown in the browser; within this tag can be defined properties common to all the site, as background color and margins. Within the body <body> can find many labels.
• Most labels must be closed as open, but with a slash (“/”)

SOURCES:

• http://es.wikipedia.org/wiki/C%C3%B3digo_HTML