El Proyecto de Carta para la Preservación del Patrimonio Digital elaborada por la UNESCO defiende con argumentos convincentes la necesidad de la preservación digital. Y es que, hay que tener en cuenta que nuestro patrimonio cultural, científico y de información se da cada vez más en formas digitales cuya creación y posterior aumento de volumen ha sido causado por el creciente empleo de la informática y de la telemática.  Lo que ocurre es que existen graves problemas para mantener utilizable y disponible este creciente patrimonio digital. Los medios que se utilizan para su almacenamiento no son nada estables y la tecnología que se necesita para poder acceder a él es constantemente superada por otras nuevas.

La definición que aporta la UNESCO sobre el Patrimonio Digital está orientada a la educación y a la cultura:

Patrimonio es nuestra herencia del pasado, nuestros bienes actuales y lo que legamos a las generaciones futuras. El patrimonio es, o debería ser, algo que se transmite de generación en generación porque se valora.

Se podría definir el patrimonio digital como el conjunto de objetos, creados de forma numérica o procedentes de la digitalización de documentos analógicos, que tienen un valor perdurable para una comunidad. Estos materiales precisan de un sistema informático para ser producidos y consultados y están compuestos por información textual, icónica, sonora, programas de ordenador o por una combinación de todas o algunas de estas categorías, en cuyo caso recibe la denominación de multimedia, codificada en formato digital.

SELECCIÓN, RECOPILACIÓN Y CONSERVACIÓN DE LAS PUBLICACIONES DIGITALES:

Antes de comenzar a recopilar el patrimonio digital es necesario saber qué se quiere conservar y para qué se hace. El fin es facilitar su acceso y consulta a los usuarios en la sede física de la biblioteca, en sus domicilios o en sus lugares de trabajo a través de las redes de ordenadores y telecomunicaciones.

Los objetos que pretendamos reunir dependerán de varios factores como el tipo de biblioteca, la comunidad a la que atiende y la forma de producción de los documentos.

Los objetivos más evidentes de la selección son constituir una colección de materiales bibliográficos que ayuden a cumplir con la misión de la institución a la que la biblioteca sirve, a colaborar en la consecución de sus objetivos y a satisfacer las necesidades recreativas, formativas, informativas y/o de investigación de los usuarios a los que atiende.

Una vez seleccionados los objetos, la siguiente actuación consistirá en su adquisición. Es conveniente mencionar que los procedimientos que son utilizados para los materiales bibliográficos tradicionales también se pueden aplicar a los electrónicos que constituyen el patrimonio digital. Se podrán comprar, obtener a través de la normativa de depósito legal, intercambiar con otra institución una copia de un documento analógico digitalizado, e, incluso, aceptar su donación siempre que se adecúe a los fines y servicios de la biblioteca y a las condiciones técnicas y legales que ésta estipule.

La recepción de la publicación en el repositorio diferirá según se trate de:

• Objeto digital tangible (CD-ROM, DVD…) cuyas tareas para ser ingresado son las mismas que las llevadas a cabo con los documentos convencionales. La única diferencia es que su contenido se vuelca en un soporte magnético para facilitar su acceso y difusión a través de una red de comunicaciones.
• Accesible en línea donde podemos diferenciar dos formas de transferir la publicación. La primera, donde el sujeto obligado transmite la publicación al repositorio. Y, la segunda, en la que le autoriza a acceder a su sistema informático para descargar el documento.

Es importante destacar que el patrimonio digital presenta diversas dificultades que han de tenerse en cuenta:

• La gran cantidad de material a evaluar.
• Hay numerosos medios que son utilizados para producir y difundir los materiales digitales.
• El tiempo de realización es decisivo.
• Algunos objetos digitales son difíciles de definir.
• Puede que determinar la procedencia de los materiales digitales no sea fácil.

A los diversos aspectos relacionados con la duración de los soportes y de los documentos en línea, las condiciones medioambientales que precisan así como a medidas técnicas y legales que hagan posible la preservación del patrimonio digital, es necesario añadir la conveniencia de que el repositorio disponga de garantías de seguridad informática y contra las catástrofes naturales, sin olvidar las incidencias electromagnéticas que pueden afectar al normal funcionamiento de los equipos y a las distintas clases de memorias masivas en las que se almacenan los datos. Por ello, es aconsejable que además de la reescritura periódica de los datos en los nuevos soportes, se realicen copias de seguridad de la información que serán almacenadas en lugares diferentes de la sede del repositorio digital.

Sources:

*Directrices para la Preservación del Patrimonio Digital. Retrieved 19:45, 13/11/09 from http://unesdoc.unesco.org/images/0013/001300/130071s.pdf

*La Recopilación y Preservación del Patrimonio Digital. Retrieved 20:27, 13/11/09 from http://bv.gva.es/documentos/Ponencias/Melero.pdf

Los documentos digitales son aquellos que se registran en un medio electrónico a través de codificaciones que se basan en el uso de combinaciones de señales eléctricas positivas y negativas Además, en los documentos digitales no importa cúal sea la forma de la información; ya puede ser texto, sonido, imagen fija o en movimiento,

Los documentos digitales pueden diferenciarse de los documentos tradicionales porque poseen una serie de características que permiten su diferenciación. A continuación voy a describir algunas de esas características comparándolas con las de la forma tradicional.

Mientras que el documento tradicional implica una lectura pasiva, el documento electrónico permite que el usuario efectúe cambios en él. En el documento tradicional, el lector elabora a partir de lo que percive, no puede preguntar sobre el contenido, no le permite agregar nuevos contenidos a lo ya establecido en el propio documento. Enb definitiva, el lector no puede aportar nada al documento establecido. Sin embargo, el documento electrónico da la opción de preguntar por el contenido, añadir comentarios, incluso modificar o agregar contenidos. Por otro lado,  el lector tiene la posibilidad de cambiar aspectos del formato.

El documento tradicional exige una lectura lineal, es decir que el lector cuando comienza la lectura debe leer desde el comienzo hasta el final, mientras que el documento digital permite establecer relaciones entre una palabra o una frase de un punto concreto del documento. Además, este tipo de documentos permiten combinar texto con sonido, sonido con imágenes, imágenes con texto, etc.

Es importante destacar que el hecho de que el documento digital se produzca en el mismo medio en que se publica (el medio electrónico) facilita efectuar cambios en él sin que sea necesario reproducirlo por entero como ocurre con el documento tradicional. Esto ayuda a la corrección de los textos.

The concept of shared online bookmarks dates back to April 1996 with the launch of itList. But what is social bookmarking? Wikipedia defines social bookmarking as “a method for Internet users to share, organize, search, and manage bookmarks of web resources”: that is, the practice of classifying resources by the use of informally assigned, user-defined keywords or tags. These services allow the user not only to collect his favourite resources online, but they also give the option to keep them. What’s more, each user can consult the collection of other users and they can also add them to their own selection. As a result, resources can be shared among different people and they can be easily distributed. What happens is that if you are searching for a particular subject, other users may have bookmarks that match up with exactly what you were looking for or interests you. This is due to the fact that you can see the collective list of resources from all the users who share the same research interest. In this kind of services, bookmarks that reference the resources are shared, but not the resources themselves.

Each user has the option to add a description to the bookmark, or votes in favour or against, or even tags (which become a folksonomy). That way, other users are able to know what the resource is about instead of having to download it for understanding the content.

* http://denham.typepad.com/km/2005/01/social_bookmark.html

* http://eibar.org/blogak/prospektiba/es/archive/2005/02/20/178

* http://en.wikipedia.org/wiki/Social_bookmarking

* http://dlib.org/dlib/april05/hammond/04hammond.html

* http://article.pubs.nrc-cnrc.gc.ca/RPAS/rpv?hm=HInit&calyLang=eng&journal=jchla&volume=27&afpf=c06-024.pdf

         MT systems offer the possibility of translating a text or a fragment (no more than 1000 characters) for free. However, if we focus our attention in the translation we would realise that there are several mistakes.
        Those mistakes are related to grammar mistakes, word order, wrong words or even whole sentences, change in the meaning, etc.
I have chosen a fragment about Global Warming taken from wikipedia and I’ve used Translendium to translate the text:

“Global warming is the increase in the average measured temperature of the Earth’s near-surface air and oceans since the mid-twentieth century, and its projected continuation.
Although the term has become popular with the public and media, according to the National Academy of Sciences, “the phrase ‘climate change’ is growing in preferred use to ‘global warming’ because it helps convey that there are [other] changes in addition to rising temperatures.” Climate change refers to any significant change in measures of climate (such as temperature, precipitation, or wind) lasting for an extended period (decades or longer). Global warming is an average increase in the temperature of the atmosphere near the Earth’s surface and in the troposphere, which can contribute to changes in global climate patterns. Global warming and climate change can be caused by a variety of factors, both natural and human-induced.[1]
Increasing global temperature is expected to cause sea level to rise, an increase in the intensity of extreme weather events, and significant changes to the amount and pattern of precipitation. Other expected effects of global warming include changes in agricultural yields, modifications of trade routes, glacier retreat, species extinctions and increases in the ranges of disease vectors.
Remaining scientific uncertainties include the amount of warming expected in the future, and how warming and related changes will vary from region to region around the globe. Most national governments have signed and ratified the Kyoto Protocol aimed at reducing greenhouse gas emissions, but there is ongoing political and public debate worldwide regarding what, if any, action should be taken to reduce or reverse future warming or to adapt to its expected consequences.”

El calentamiento global es el aumento en la temperatura medida media del aire de cerca superficie de la Tierra y los océanos desde el siglo de mid-twentieth, y su continuación proyectada.
Aunque el plazo|término se ha vuelto popular con el público y medios de comunicación, según el Nacional Academy of Sciences , “la frase|locución ‘cambio climático’ está aumentando en el uso preferido” a ‘calentamiento global’ porque ayuda a transmitir eso hay [otro] cambios además de temperaturas crecientes. ” El cambio climático se refiere a cualquier cambio significativo en medidas de clima (como temperatura, precipitación, o viento) que dura para|por un periodo extendido (décadas o más mucho tiempo). El calentamiento global es un aumento medio en la temperatura del ambiente/atmósfera cerca de la superficie de la Tierra y en la troposfera, que puede contribuir a cambios en patrones de clima globales. El calentamiento global y cambio climático pueden ser causados por una variedad de factores, los dos natural e induced.[1 humano]
Se espera que la temperatura global creciente cause nivel del nivel del mar para levantarse/subir, un aumento en la intensidad de acontecimientos de tiempo extremos, y cambios significativos para la cantidad y patrón de precipitación. Otros efectos esperados de calentamiento global incluyen cambios en rendimientos agrícolas, modificaciones de rutas comerciales, retirada de glaciar, extinciones de especie y aumentos en las series de vectores de enfermedad.
Las incertidumbres científicas restantes incluyen la cantidad de apreciar/calentar esperada en el futuro, y cómo apreciando/calentando y siendo relatado variarán los cambios de región a región alrededor del globo. Han firmado la mayor parte de gobiernos nacionales y ratificado el Protocolo de Kyoto apuntado a emisiones de gas de efecto invernadero que reducen, pero hay debate político y público en curso mundialmente en cuanto a qué, si hay, se debería considerar acción que redujera o invirtiera en futuro calentar o se adaptara a sus consecuencias esperadas.
 
       As we can see, there are some problems related to the word order; medida media should be “media medida”; más mucho is an inpossible order in Spanish, instead the correct order is “mucho más”. In these examples we have only two words in the wrong place, but there are also problems with whole sentences; for example, “Han firmado la mayor parte de gobiernos nacionales” should be “la mayor parte de gobiernos nacionales han firmado…”; “a emisiones de gas de efecto invernadero que reducen” in the context has no sense, instead if we change the order we could understand the meaning “apuntado a reducir las emisiones de gases que producen el efecto invernadero”.
       On the other hand, we can also find problems articles, that in some cases are omitted: “de cerca de superficie” needs an article “de cerca de la superficie”.

       We can also find some words that are not translated: mid-twentieth or induced.
       And, finally, wrong sense of the word in the sentence: “proyectada” in this case would be better “presvista o planeada”. There are also some cases were we are given two options, but one of them does not suit because of the meaning of the sentece: “plazo/término” in this case should be “término”,”ambiente/atmósfera” better “atmósfera”, etc.

Sources:

* Translendium. Retrieved 16:05, 19 June, 2008 from http://www.translendium.net:8080/home/text.do
*Global warming. (2008, June 19). In Wikipedia, The Free Encyclopedia. Retrieved 19:50, June 19, 2008, from http://en.wikipedia.org/w/index.php?title=Global_warming&oldid=220411732

     Machine Translation, sometimes referred to by the abbreviation of MT, is a sub-field of comptutational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, 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.

Current machine translation software often allows for customization by domain or proffesion (such as weather reports) improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic languages is used. It follows then that the machine of governement and legal documents more readily produces usable output than conversation or less standardized text.

     Computer-assisted translation, computer-aided translation or CAT, 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 a broad and imprecise term cobering range of tools, from the fairly simple to the more complicated. These can include: spell checkers, grammar checkers, terminology manager dictionaries on CD-rom, terminology databases, full-text search tools, project management software, translation memory managers (TMM). Systems that are nearly automatic as in machine translation, but allow user decisions for ambiguous cases. These are called sometimes human-aided machine translation. Some advanced computer-assisted translation solutions include controlled machine translation (MT). Integration of MT into computer-assisted translation has been implemented in various ways by various parties. Although this type of technology is neither widely known nor available to individual translators, carefully customized user dictionaries based on correct terminology significantly improve the accuracy of MT, and as a result, they improve the efficiency of translation process.

     Content management or CM is a set of technologies that the evolutionary life cycle of digital information. This digital information is also referred to as content or, to be precise, digital content. Digital content may take the form of text, such as documents, multimedia files, such as audio or video files, or any other file type that follows a content life cycle which requires management. The digital life content consists of 6 primary phases: create, update, publish, translate, archive and retrieve. A critical aspect of content management is the ability to manage versions of content as it evolves. Authors and editors often need to restore older version of edited products due to a process failure or an undesirable series of edit.

     Translation technology covers all the systems that are used for the translation process. Translation is the action of interpretation of the meaning of a text, and subsequent production of an equivalent text, also called a translation, that communicates 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”; the final product is sometimes called the “target text”. Translation must take into account constraints that include context, the rules of grammar of the two languages, their writing conventions, and their idioms. With the advent of computers, attempts have been made to computerize or otherwise automate the translation of natural-language texts (machine translation) or to use computers as an aid to translation (computer-assisted translation).

Sources:

*Machine translation. (2008, April 19). In Wikipedia, The Free Encyclopedia. Retrieved 14:47, April 21, 2008, from http://en.wikipedia.org/w/index.php?title=Machine_translation&oldid=206756422

*Computer-assisted translation. (2008, April 18). In Wikipedia, The Free Encyclopedia. Retrieved 14:48, April 21, 2008, from http://en.wikipedia.org/w/index.php?title=Computer-assisted_translation&oldid=206481669

*Content management. (2008, April 19). In Wikipedia, The Free Encyclopedia. Retrieved 14:49, April 21, 2008, from http://en.wikipedia.org/w/index.php?title=Content_management&oldid=206669339

*Translation. (2008, April 21). In Wikipedia, The Free Encyclopedia. Retrieved 14:49, April 21, 2008, from http://en.wikipedia.org/w/index.php?title=Translation&oldid=207111486 

     This article gathers and summarizes the characteristics of a translation task according to the FEMTI report. As this report points out, the characteristics of the translation task refers to the information flow intended for the output, from the point of view of the agent (human or otherwise) who receives the translation.

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.

Dissemination: the ultimate purpose of dissemination is to deliver to others a translation of douments produced inside the organization.

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.

Sources:

* Eduard Hovy, Margaret King and Andrei Popescu-Belis. (2003). FEMTI. Retrieved 15:23, April 19, 2008, from http://www.issco.unige.ch:8080/cocoon/femti/printable.html

     Speech synthesis is an area in which research is being carried out by the Austrian Research Institute for Artificial Intelligence (OFAI).

     We can point out the explanation that Wikipedia gives about this topic. Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.

Synthesized speech can be created by concatenating pieces of recorded speech that are sotred in database. Systems differ in the size of the stored speech units. A synthesizer can incorporate a model of the vocal track and other human voice characteristics to create a completely “synthetic” voice output.

     Thierry Dutoit explains  that the ultimate goal of a text-to-speech (TTS) synthesizer is to read any text, whether it was directly introduced in the computer by an operator or scanned and submitted to an optical character recognition (OCR) system. Reading should be intelligible and natural.

In reference to the definition of text-to-speech systems Dutoit points out that specific talking machines termed as voice response systems produce artificial speech by simply concatenating isolated words or parts of sentences. They are, however, applicable only when a limited vocabulary is required and when the sentences to be pronounced share a very restricted structure, as is the case for the announcement of arrivals in train stations, for instance. In the context of TTS synthesis, it is impossible to record and store all the words of the focus language. It is thus more suitable to define text-to-speech as the production of speech by machines, by way of automatic phonetization of the sentences to utter.

Sources:

*Speech synthesis. (2008, April 10). In Wikipedia, The Free Encyclopedia. Retrieved 14:04, April 13, 2008, from http://en.wikipedia.org/w/index.php?title=Speech_synthesis&oldid=204741034

*Thierry Dutoit. “An Introduction to Text-to-Speech Synthesis”. Published in 1997, Springer. 285 pages. Retrieved 13:46, April 13, 2008 from http://books.google.com/books?hl=en&lr=&id=bTmWkXi1e90C&oi=fnd&pg=PR13&dq=an+introduction+to+text-to-speech+synthesis&ots=Ik9kl1XoPC&sig=dYqHTVQuexMyMU0OxUbOoNKSJg0

      In this second article about the explanations of some of the topics that deal with Human Language Technologies, I’ll focus on another area where the Language Technology Group conducts research: Machine Learning.

   As an introduction we can mention what Wikipedia points out about this topic. Machine learning is a subfield of artificial intelligence that is concerned with the design and development of algorithms and techniques that in some way make the computers learn. As Martin Sewell mentions, in practice, this involves creating programs that optimize a performance criterion through the analysis of data. 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. Among all the applications that machine learning has, wikipedia mentions the following: 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.

       According to Ethem Alpaydin, in machine learning the approach is to collect a large collection of sample utterances from different people and learn to map these to words. Already, there are many successful applications of machine learning in various domains: there are comercially available systems for recognizing speech and handwriting.

Machine learning is not just a database problem; it is also a part of artificial intelligence as said above. To be intelligent, a system that is in a changing environment should have the ability to learn. Machine learning also helps us find solutions to man problems in vision, speech recognition, and robotics.

Sources:

*Martin Sewell. “Machine Learning”. (2007). Retrieved 22:45, April11, 2008, from http://www.machinelearning.net/machine-learning.pdf 

*Ethem Alpaydin. “Introduction to Machine Learning”. Published in 2004, MIT Press. 415 pages. Retrieved 23:12, April 11, 2008, from http://books.google.com/books?hl=en&lr=&id=1k0_-WroiqEC&oi=fnd&pg=PR13&dq=introduction+to+machine+learning&ots=p8XJTOgLyQ&sig=o-HYojpOGv2AVM5voMAe0WHibjw

*Machine learning. (2008, April 1). In Wikipedia, The Free Encyclopedia. Retrieved 14:01, April 12, 2008, from http://en.wikipedia.org/w/index.php?title=Machine_learning&oldid=202557440

     In this article I’m going to develop the concept of “Text mining” on which the Language Technology Group conducts research.

     The definition that Wikipedia gives about Text Mining is useful as a first step on understanding what does that concept refer to. Text mining refers generally to the process of deriving high quality information from text. High quality information is derived through the dividing of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 

     We can point out another definition given by Marti Hearst in his article “What is Text Mining?” . He refers to this term as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. A key element is the linking together of the extracted information together to form new facts or new hypothesis to be explored further by more conventional means of experimentation.

In text minig, the goal is to discover heretofore unknown information, something that no one yet knows and so could not have yet written down.

A typical example in data mining is using consumer purchasing patterns to predict which products to place close together on shelves, or to offer coupons for, and so on. A related application is automatic detection of fraud, such as in credit card usage. Analysts look across huge numbers of credit card records to find deviations from normal spending patterns.

     To go deeper into what is text mining we could make reference to what Ronen Feldman and James Janger explain in their book “The text mining handbook: advanced approaches in analyzing unstructured data”. Text Mining is a new research area that tries to solve the information overload problem by using techniques from data mining, machine learning, natural language processing (NLP), information retrieval (IR), and knowledge management. Text mining involves the preprocessing of document collections (text categorization, information extraction, term extraction), the storage of the intermediate representations, the techniques to analyze these intermediate represenations (such as distribution analysis, dustering, trend analysis, and association rules), and visualization of the results.

Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time using a suite of analysis tools. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns. In the case of text mining, however, the data sources are document collections, and interesting patterns are found not among formalized database records but in the unstructured textual data in the documents in these collections.

For text mining systems, preprocessing operations center on the identification and extraction of representative features for natural language documents. These processing operations are responsible for transforming unstructured data stored in document collections into a more explicity structured intermediate format, which is a concern that is not relevant for most data mining systems. 

Sources:

*Ronen Feldman and James Sanger. “The text mining handbook: advanced approaches in analyzing unstructured data”. Published in 2006, Cambrige University Press, 410 pages. Retrieved 22:15, 11 April, 2008 from http://books.google.com/books?hl=en&lr=&id=3PcEoz48RBcC&oi=fnd&pg=PR10&dq=the+text+mining+handbook&ots=dDUECB3_k4&sig=yoHE5tmdhlhZ2q8Qb9N0Spwywxc

*Marti Hearst. “What is Text Mining?”. (October 17, 2003). Retrieved 22:05, 11 April, 2008, from http://www.jaist.ac.jp/~bao/MOT-Ishikawa/FurtherReadingNo1.pdf

*Text mining. (2008, April 8). In Wikipedia, The Free Encyclopedia. Retrieved 11:52, April 12, 2008, from http://en.wikipedia.org/w/index.php?title=Text_mining&oldid=204166609