Un documento tradicional, es cualquier unidad significativa de información que este en un soporte que permita su almacenamiento y su recuperación; me estoy refiriendo a revistas, libros o incluso artículos en los que se van a  publicar cartas, etc.

Por otra parte, un documento digital, es una unidad significativa independiente de información, pero almacenada en un disquette, un cd, etc. En este tipo de documentos la información se registra en un medio digital a través de codificaciones, que se representan mediante dígitos; 0 para negativo, y 1 para positivo. La propiedades del documento digital son las siguientes; computabilidad, virtualidad y capacidad.

 Los documentos analógicos o tradicionales tienen numerosas limitaciones frente a los documentos digitales;ya que, por ejemplo, no pueden contener ni videos, ni sonidos, ni imagenes animadas. Otra de las muchas ventajas de el domento digital es que el coste de la información es cero, y que la capacidad del documento digital puede ser mucho mayor que el de los documentos tradicionales.

Por otra parte, los documentos analógicos tienen otra ventajas que los digitales no tienen; ya que los tradicionales tienen lo que se llama “impacto directo”, es decir, que no se necesita un aparato para leerlos, pero en cambio, los digitales hacen uso de aparatos que con los años se pueden quedar fuera del mercado. Aparte de eso, un solo bit erróneo puede hacer que un documento digital de cién hojas no se pueda leer.

En mi opinión, después de haber comentado las ventajas y desventajas de estos dos tipos de documento, podemos observar que tanto uno como el otro tienen ventajas indiscutibles, pero va a ser el documento digital el que gane en importancia, especialmente en el futuro.

BIBLIOGRAFÍA

• Sistemas de información: las propiedades de la información digital. Retrived,  November 1, 2009, from http://www.elprofesionaldelainformacion.com/contenidos/2001/diciembre/5.pdf
• Documentos digitales: A. definición y características. Retrieved, November 1, 2009, from http://www.msinfo.info/propuestas/documentos/documentos_digitales.html

Social bookmarkings are an innovation of the last years, but what is teh correct definition for this method? Wikipedia explains that it is “a method for Internet users to share, organize, search and manage bookmarks of web resources”; one of the examples of social bookmarking is the page that we are using in class, citeULike; or even the is a most famous one, Facebook.

In a system of social bookmarking, people puts in a page of internet a list of interests that they want to share with other people, or resources that they just want to remember or recolect; so that, everyone can see them. These methods can be public or private; if they are public, everybody how enters that page could see those resources; besides, if they are private, only a special group of people can see them. It is usual to recommend tags, which are a way of classfying the resources, this way, it is easier for users to find what they want.

At first this web-pages were not as popular as they are nowadays, specially among young people; so, as the competence has grown up, the services have started putting remarks, to create groups, to add notes and so for.

This phenomenon has become so useful and popular nowadays, however, it has some disadvantages, specially referring to the tags, which sometimes can offer bad ortographical results; nevertheless its advantages are very clear, and that is why this method has become so accepted among people of all ages.

SOURCES

• Retrieved on october 22, from en.wikipedia.org/wiki/Social_bookmarking

         I have made a translation of the text “the belle and the beast”, from french to spanish; these two languages are closely related languages, this are the probles that I have found in the tranlation, which has been made by the program call Translendium.

         The first mistake is in the title, Translendium have translate it into spanish as ” la bonita y el animal”, this is totaly wrong, because, the real translation will be ” la bella y la bestia”. Apart from that, I have to mention that he uses the word ” bonita” all the time, and it is totally wrong, as i have said before. There the same problem with other characters like “lumière” which is translated like “luz”, this is wrong, specially because it is a name, so it hasn´t got a translation.

        There are other phrases during the text that they are incorrect too, such as, “… persigue a bonita de sus asiduidades pero esconde….”, this phrase hasn’t got any sense, it is totally wrong; the real and the understable translation will be “….el le persigue a bella por sus asiduidades pero ella se esconde…”, there are lots of phrases like that along all teh text.

Finally, some words that have different meanings in the text, for example, llave/clave which in french will be “clés“, that can be “llave” or “clave“, butin this case the correct one will be clave, there are other examples such as, chica/hija, ….. 

SOURCES:

• ” la bonita y el animal”. Retrieved  the 15 of june 2008, at 21:00, from http://littera.deusto.es/enwiki/index.php/User:ZiortzaVivanco/hlt0708/fres2esen

• “la belle et la bête”. Retrieved  the 15 of june 2008, at 21:00, from http://fr.wikipedia.org/wiki/La_Belle_et_la_B%C3%AAte_%28Disney%29   

•  Translendium. Retrieved  the 15 of june 2008, at 21:00, from http://www.translendium.net:8080/home/index.jsp?locale=es

These are the differences between some specialized terms:

• Machine tranlation: sometimes referred to by the abbreviation MT, is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. Translation proper is performed by a computer,even if the human helps by preediting, postediting or answering questions to disambiguate the source text.

• Computer-assisted translation: is a form of translation wherein a human translator translates texts using computer software designed to support and facilitate the translation process. Computer-assisted translation is sometimes called machine-assisted, or machine-aided translation.

• Multilingual content management: Contains information, including also video clips, audio clips and images.

• Translation: is the action of interpretation of teh meaning of a text,and subsequent production of anequivalent text, also called translation, that communicates teh same message in another language. It must take into account constraints that include context, the rules of grammar of the two languages, their writing conventions, and their idioms.

SOURCES:

• Machine tranlation (MT). Retrieved in june 8, 2008, 20:00, http://cslu.cse.ogi.edu/HLTsurvey/ch8node6.html

• Machine translation (MT). Retrieved in june 8, 2008, 20:15,                                                   http://en.wikipedia.org/wiki/Machine_translation

• Computer-assisted translation (CAT). Retrieved in june 8, 2008, 20:30, http://en.wikipedia.org/wiki/Computer-assisted_translation

• Multilingual content management. Retrieved in june 8, 20:45, http://www.kitsite.com/articles/multilingual-content-management.html

• Translatin. Retrieved in june 8, 21:00, http://en.wikipedia.org/wiki/Translation

        I´m going to use an MT system which is called Translendium to make a translation example from spanish into english, I mean, into a less related language and then I’m going to explain the gramatic errors and other errors that the tranlation programmes make. 

        As we can see,the first two lines of the spanish version are different in the english version, the programme hasn´t understand very well the spanish version so that the order of the words in the english has been change incorrectly, that´s why it is incorrect and it has no sense.

         Apart from that, some words in spanish have more than one meaning in english so in the tranlation appear all of the words, in this case the both words “step/crossing”.

       The same happens with the subjects; in spanish sometimes there aren’t subjects so in the translation appear different subjects “its/ his/her/ their…”, maybe because, in spanish, the subject isn’t very clear.

Finally, I have to mention other error that appear in the last paragraph, in the spanish version appear “… por una serie de murallas construidas y …” but in the english one the word “…una serie…” is translated into “series” and that is incorrect, because it is not the same thing.

RESOURCES:

• Translendium programme. http://www.translendium.net:8080/home/index.jsp?locale=es, fecha de la consulta el 11 de mayo del 2008, hora de la consulta las 18:00.

• http://littera.deusto.es/enwiki/index.php/User:ZiortzaVivanco/hlt0708/eg1esen, fecha de la consulta el 11 de mayo del 2008, hora de la consulta las 17:30.

• http://es.wikipedia.org/wiki/Gran_Muralla_China, fecha de la consulta el 11 de mayo del 2008, hora de la consulta las 17:45.

           According to the FEMTI report, characteristics of a translationtask refers to the information flow intended for the output, from the point of view of the human or other thing who receives the translation.

           As the FEMTI report says, there are three characteristics of a translation task; assimilation, dissemination and communication. I´m going to explain a bit all of them:

• Assimilation: The ultimate purpose of the assimilation task is to monitor a large volume of texts produced by people outside the organization, in several laguages.

• Dissemination: The ultimate purpose of the dissemination task is to deliver to others a translation of documents produced inside the organization.The translation quality required is generally high, but translation speed is usually not a factor.

• Communication: The ultimate purpose of the communication task is to support multi-turn dialogues between people who speak different languages. The translation quality must be high enough for painless conversation, despite possible syntactically ill-formed input and idiosyncratic word and format usage.

           RESOURCES:

• FEMTI, a Framework for Machine Translation Evaluation in Isle, http://www.isi.edu/natural-language/mteval/, fecha de la consulta el 8 de mayo del 2008, hora de la consulta las 18:00.

         I’ m going to explain three of those research topics.

         First of all, I’m going to talk about Corpus Linguistics. The word Corpus may be used to refer to any text in written or spoken form. However in moder linguistics thisterm is used to refer to na large collection of texts which represent a sample of a particular variety or use of language that are presented in machine readable form. On the hand, we have to refer to Corpus Linguistics, which is now seeing as the study of linguistic phenomena through large collections of machine- readable texts: corpora. These are used within a number of research areas going from the Descriptive Study of the Syntaxis of a Language to Prosody or Language Learning, to mention but a few. Th use of real examples of texts in the study of language is not a new issue in the history of linguistics. However, Corpus Linguistics has develop considerably in the lst decades due to the great possibilities offered by the processing of natural language with computers.

            Secondly, I have to mention Semantics, which is the study of meanings in communication. In linguistics is the interpretation of sings as used by agents or communities within particulr circumstances and contexts. Semanticists differ on what constitutes meaning  in an expression. Tradicionally , the formal semantic view restricts semantics to each literal meaning, and relegates all figurative associations to pragmatics, but this distinction is increasingly difficult to defend. The degree to which a theorist subscribes to the literal- figurative distinction decreases as one moves from the formal semantic, semiotic, pragmatic, to the cognitive semantic traditions.

         Finally, I’m going to mention  Computer Assisted Language Learning (CALL), is an approach to language teaching and learning in which computer technology is used as an aid to the presentation, reinforcement and assessment of material to be learned, usually including a substancial interactive element. The philosophy of this center is that the lessons should allow the learners to learn on their own using structure and/or unstructured interactive lessons. This lessons crry two important features: bidirectional (interactive ) learning and individualized learning. CALL is not a method. It is a tool that helps teachers to facilitate language learning process.

RESOURCES

• Computed Assisted Language Learning (CALL). http://en.wikipedia.org/wiki/Computer-assisted_language_learning, fecha de la consulta 19 de marzo del 2008, hora de la consulta las 14:00.

• Semantics. http://en.wikipedia.org/wiki/Semantic, fecha de la consulta el  19 de marzo del 2008, hora de la consulta 13:15.

• Definition of Corpus. http://www.ling.lancs.ac.uk/monkey/ihe/linguistics/corpus2/2defin.htm, fecha de la consulta el 19 de marzo del 2008, hora de la consulta als 12:30.

• Corpus Linguistics. http://www.essex.ac.uk/linguistics/clmt/w3c/corpus_ling/content/introduction.html, fecha de la consulta el 19 de marzo del 2008,hora de la consulta las 12:00.

     Here are some ofthe research topics of three European research centers:

     First of all, The German Language Technology Lab., which themes are elaborated in research, development and commercial projects:

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

      Secondly, we can mention the projects of The Edinburgh Lnguage Technology Group, which conducts research and development in different areas.

• Combining shallow semantics and domain knowledge.
• Text mining for Biomedicational Content Curation.
• Cross- retail Multi- agent Retail Comparison.
• Smart Qualitative Data: methods and Community Tools for Data Mark-up.
• Machine Learning for Name Entity Recognition.
• Named entity tagging of historical parliamentary proceedings.
• Integrated Models and Tools for Fine- Grained Prosody in Discourse.
• Joint Action Science and Technology.
• AMI consortion projects that are developing technologies for meeting browsing and to assit people participating in meetings from a remote location.
• Study of how pairs collaborate when in planning a group on a map

       And finally,  The National Centre for Language, which research areas are the following ones:

• CALL Computer Assisted Language Learning: Integrating CL/NLP/HLT Technology into CALL, CALL for Endangered Languages, CALL for Primary School Environments, CALL for Remedial Learners.
• Corpus Linguistics: Collocation, Contrastive Computational Linguistics, Corpus- based Traslation Studies.
• Machine Translation and Translation Technology: SMT, RBMT, EBMT, TMs, MAT, CAT.
• Treebank- Based Unification Grammar Acquisition: Automatic Feature Structure Annotation Algorithms, Subcategorisation Frame Straction,…
• Semantics: Discourse Representation Theory, Linear- Logic Based Semantics, Computational of Logic Forms from Treebanks, Open- Domain Question Answering Systems.
• Speech Technology: Speaker Characterisation, Audio Classification, Retrieval and Coding, Human Computer Interfaces (HCIs).
• Multilingual Information Retrieval/Extraction.
• Language Evolution.

RESOURCES:

•  The National Centre for language Technology. http://www.computing.dcu.ie/research/nclt/research.html, fecha de la consulta el 14 de marzo del 2008, hora de la consulta 20:00.

• The Edinburgh  Language Technology Group. http://www.ltg.ed.ac.uk/, fecha de la consulta el 14 de marzo del 2008, hora de la consulta 20:15.

• The Language Technology Lab. http://www.dfki.de/lt/projects.php, fecha de la consulta el 13 de marzo del 2008, hora de la consulta 19:30.

There are some European research centers for Human Language Technologies, here are three examples:

• Language Technology Lab., Germany.

           Their objective 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 needs of the users and the demands of the market. 

• National Centre for Language Technology, Ireland.

               This centre conducts the research into the processing of Human Language through computers, for example speech recognition and synthesis, machine translation and others.

• Edinburgh Language Technology Group, Scotland, UK.

           Is a research group that has been studying in the area of natural engineering  since the early 1990s.

          They focus on building practical solutions to real problems in text processing.

RESOURCES.

• http://www.computing.dcu.ie/research/nclt/, fecha de la consulta el 27 de marzo del 2008, hora de la consulta las 19:20

• http://www.ltg.ed.ac.uk/, fecha de la consulta el 27 de marzo del 2008, hora de la consulta las 19:30

• http://www.dfki.de/lt/projects.php, fecha de la consulta el 27 de marzo del 2008, hora de la consulta 20:00

Hans Uszkoreits is Proffesor of Computational Linguistics of Saarland University and he also serves as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) where he heads the DFKI Language Technology Laboratory. Apart from that he is Proffesor of the Computer Science Department.

      He has studied Linguistics and computer science at the Technicl University of Berlin and the University of Texas in Austin. During part of his live he has been working as a research associate in a large machine translation project at the Linguistics Research Center,he also worked as a computer scientist at the Artificial Intelligence Center of SRI International in Menlo Park and in other similar ones.

    Hans is member of lots of academies, such as, permanent member of the International Committee of Computational Linguistics (ICCL) , Member of  the European Academy of Sciences, member  of the Executive Board of the European Network of Language and Speech and more and more.    

      Here are some of the recent publications of this proffesor :

• Uszkoreit, H. (2007) Methods and Applications for Relation Detection. In: Proceedings of the Third IEEE International Conference on Natural Processing and Knowledge Engineering, Beijing, 2007.

•    Uszkoreit, H. F. Xu, W. Liu (2007) Challenges and Solutions of Multilingual and Translingual Information Service Systems , To appear in Proceedings of HCI International 2007, 12TH International Conference on Human- Computer Interaction, Beijing, 2007.

• Busemann, S. and H. Uszkoreit (2004) Predicting the Future: Technology Roadmapping. In: ELSNews, (3) 2004.

         RESOURCES:

• www.dfki.de/it/it-general.php, fecha de la consulta el 27 de marzo del 2008, hora de la consulta las 18:00

• http://www.coli.uni-saarland.de/~hansu/, fecha de la consulta el 27 de marzo del 2008, hora de la consulta las 18:15

• http://www.dfki.de/~hansu/LT.pdf, fecha de la consulta el 26 de marzo del 2008, hora de la consulta las 20:00