In today’s post we are going to discover what digital libraries are. We can define them as libraries in which collections are stored in digital formats and accesible by computers. We can affirm that digital libraries are a type of information retrieval system.

Digital library concept

Moreover, one of the main ideas of this post is to present some of the most important digital libraries that nowadays exist such as Project Perseus, Project Gutenberg, Google Book Search, etc. However, as I am studying a degree in English I thought that it would be appropriate to focus on one important digital library with contents in English. That is the reason why we are going to talk today about the Darlington Digital Library (University of Pittsburgh).

The Darlington Digital Library was created from the collection of books, atlases and maps that were assembled, in the first place, by the attorney William M. Darlington, who was born in Pittsburg in 1815. After his death in 1889, it was his family that continued his passion for recollecting documents and materials about colonial American history. In 1938 the University of Pittsburgh Press published a magnificent overview of the whole collection.

In adittion, the Digital Research Library (DLR) department started digitalizing and scanning books from the Darlington collection in the year 2006. In this project there are involved scanning technicians and graduate students who spend 90 hours per week digitalizing the already mentioned materials. But would you like to know how does this process take place? Then pay attention to the following video:

As a result, what we obtain is a whole series of atlases, books, broadsides, images, manuscripts and maps that can be consulted online, at any time and all around the world. Besides, with this system of digital libraries the materials that we have access to do not suffer any harm and they are preserved in optimum conditions. So, if remained skeptical about the benefits of digital libraries give them a try. You will not regret.

References:

• Digital library. (2009, October 22). In Wikipedia, The Free Encyclopedia. Retrieved 18:10, October 25, 2009, from
  http//en.wikipedia.org/w/index.php?title=Digital_library&oldid=321357328
• List of digital library projects. (2009, October 21). In Wikipedia, The Free Encyclopedia. Retrieved 18:13, October 25, 2009, from
  http://en.wikipedia.org/w/index.php?title=List_of_digital_library_projects&oldid=321196444
• The Darlington Digital Library. (2007). In The Darlington Digital Library, University of Pittsburgh. Retrieved: 19:17, October 25, 2009, from
  http://digital.library.pitt.edu/d/darlington/index.html

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

The quick development of  new technologies is transforming our lives dramatically in many aspects. One of them is literature. Nowadays, traditional books are living a little crisis due to the appearance of eBooks. An eBook (or electronic book) is, in plain words, a digital version of a traditional book.

An example of eBook: the new Sony Reader (model PRS-505)

So now you may be wondering why you should start using this new type of devices, right? The answer is quite simple: because they have a never ending list of advantages over traditional books. Let me illustrate this afirmation with a few examples:

In the first place, we have to highlight the enormous capacity of an eBook reader. Just one of these appliances is able to store hundreds of books. Can you believe it? 100 books in a 250 grams gadget! Secondly, you can legally download the content of a book directly from the Internet to your eBook reader, which means that you have access to constantly updated information within seconds. Moreover, all data contained in these products can be easily improven by means of adding hyperlinks, etc.

And all that we have mentioned above is just the large number of benefits that eBook readers have from the point of view of the device itself. We have to remark here too that they allow us to save a huge amount of paper, which means that reading in a screen is truly ecological and it will help to save the planet or, at least, to give it a break.

Still not convinced? Oh, well, I think you are a romantic then. However, the sooner you get used to these new appliances, the better. Traditional books are evolving after all I assume; and the future success of eBooks, audioBooks, eComics seems pretty clear. It’s just a matter of time.

References:

• E-book. (2009, October 16). In Wikipedia, The Free Encyclopedia. Retrieved 21:51, October 18, 2009, from
  http://en.wikipedia.org/w/index.php?title=E-book&oldid=320169638
• ¿Qué es un eBook?  (2009). In Todoebook.com. Retrieved: 21:36, October 18, 2009, from
  http://www.todoebook.com/que-es-un-ebook.html
• Libro electrónico. Qué es. (2003, January). In Ciberhábitat. Retrieved: 19:48, October 17, 2009, from
  http://ciberhabitat.gob.mx/biblioteca/le/
• Joseba Abaitua (2003, November 17). Panorama de la Edición digital. Jornada sobre La profesión del filólogo en la sociedad de la información (Universidad de Deusto). Retrieved:  September 28, 2009, from
  http://www.slideshare.net/JosebaAbaitua/panorama-de-la-edicin-digital

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

Nowadays, we are living a quite strong conflict of interests between the Internet users and traditional editors due to the increasing digitalization of  books since the Net was born years ago. This topic has caused  and is still today causing a big controversy among the population and that is the reason why we wanted to mention here.

CEDRO Logo

As Delia Lipszyc perfectly describes in her book Nuevos temas de derecho de autor y derechos conexos, works can now be digitalized (transformed into binary code) and be uploaded to an Internet server. As soon as these works are available online, the general public can immediatly make an unlimited number of copies without the legal permission of the copyrights holder. Moreover, once downloaded, these copies can be manipulated, processed, amplified, reproduced and transferred via electronic mail and/or public website.

From that moment, Internet users and traditional editors have started an important legal battle trying to demonstrate if the difussion of those contents is legal or not. Important associations as, for example, CEDRO (Centro español de derechos reprográficos) are fighting in order to restrict the access to these contents and to be able to charge the users some money for these intellectual rights. As a consequence, important firms such as, for instance, Google Inc. have been sentenced in court to pay multimillionaire fines to these associations and have been also forced to remove from their websites protected content.

On the other side, users claim that the Peer-to-Peer (P2P) networks are not illegal. Besides, in their opinion, the access to culture is a universal right that cannot be avoided. This controversy was analyzed and the Bern Convention established that copyrights belong to the author during their whole life and 50 years more. After that moment copyrights become public and we no longer have to pay for them.

References:

• María Jesús Lamarca. (2009, February 15). “El autor y la autoría en el mundo digital”. Retrieved: September 19, 2009, from
  http://artesadigital.blogspot.com/2009/02/el-autor-y-la-autoria-en-el-mundo.html
• El plan de Google para dominar el mundo (de los libros). (2009, May 20). In Escritores.org. Retrieved: 22:04, October 2, 2009, from
  http://www.escritores.org/proba3/index.php/recursos-para-escritores/noticias-literarias/1667-el-plan-de-google-para-dominar-el-mundo-de-los-libros-
• Los retos de los autores y editores con los cambios producidos con las nuevas tecnologías, a debate. (2008, February 8). In Asociación colegial de escritores de Catalunya. Retrieved: 21:44, October 2, 2009, from
  http://www.acec-web.org/quincenal/09/el15nalESP.htm#6
• Carlos A. Fernández Ballesteros. (2007, May). “Desafíos para el derecho de autor y los derechos conexos en la era digital”. In Seminario internacional de la OMPI sobre el uso estratégico de la propiedad intelectual para el desarrollo económico y social. Retrieved: 21:20, October 2, 2009, from
  http://209.85.229.132/search?q=cache:_j7tyDvyxLgJ:www.wipo.int/ip-development/en/economic_dev/docs/rio07_ballesteros.doc+cedro+conflicto+intereses+usuarios+y+editores&cd=10&hl=es&ct=clnk&client=safari

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

A chatterbot (chat bots, talk bots) is a type of conversational agent; it is a computer program known as Artificial Conversational Entity (ACE), which is designed to simulate a real conversation with human users.

ASIMO, created by Honda, is a humanoid robot that is able to walk and interact with humans.

And when we talk about a simulation, we really mean that because chatterbots are not capable of “understanding” the conversation. The way they “converse” is just simply by recognizing words or phrases from the human user, which allows them to use pre-calculated responses which can move the conversation on in an apparently meaningful way without requiring them to know what they are talking about.

To solve this problem researchers have been working hard in order to provide the chaterbots with some kind of Artificial Intelligence (AI); moreover, they have made an special effort to improve their flair for Natural Language Processing (NLP). As a result, we have obtained chatterbots as Jabberwacky, which is able to learn new responses based on user interactions.

Nowadays, large commercial entities are already utilizing Virtual Assistants to reduce expenditures on Call Centres and provide a first point of contact that can inform the user exactly of points of interest, provide support, capture data from the user and promote products for sale. This has to be considered an important step forward in the use of chatterbots in order to help people, but we cannot forget to mention that malicious chatterbots are also frequently used to ask people for personal information, such as bank account numbers. So we have to be on alert.

If scientists continue providing chatterbots with Artificial Intelligence (AI) may be one day we will all be able to interact and have a “real” conversation with chatterbots in a natural language. And who knows, may be that day is not so far away.

References:

• Chatterbot. (2009, March 30). In Wikipedia, The Free Encyclopedia. Retrieved 22:09, April 2, 2009, from
  http://en.wikipedia.org/w/index.php?title=Chatterbot&oldid=280590430
• What is a chatbot? (2009). In chatbots.org. Retrieved: 23:06, May 2, 2009, from
  http://www.chatbots.org/static/English/what_is_a_chatbot

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

Have you ever asked yourself what happens when you introduce a question in your computer? No? Never? Then you will get surprised with today’s post because I am going to talk about Question Answering (QA).

Question Answering (QA)

Question Answering (QA) is, in information retrieval, the task of answering automatically a question asked in natural language. In order to provide you with a proper answer a QA computer program can use either a pre-structured database or a collection of natural language documents (such as the World Wide Web, for instance).

QA computer programs are designed in order to be able to deal with a wide range of question types including: What, When, Why, Where, Who, How, definitions… Moreover, two types of question can be distinguished. In the first place, we have the closed-domain questions, which deal under one specific topic. In the second place, we have the open-domain questions, which deal with almost everything. This second type of question is much more difficult to give an answer due to the higher requirement of Natural Language Processing (NLP).

However, QA methods still suffer from some little problems. Let’s mention a few of them: QA programs have to be able to “understand” the question; they have to know how to extract the most relevant information; they have to be able to formulate an answer in natural language; they have to provide you with the requested information quickly; they have to deal with cross-lingual questions, etc.

In spite of all of this, QA computer programs are evolving extremely quickly and they are absolutely fulfilling our expectations. We are day after day provided by these QA computer programs with the requested information and the results cannot be more satisfactory. Is not that right?

References:

• Sistemas de Question-answering. (2009). In Recuperación y Organización de la Información, por la Universidad Carlos III de Madrid. Retrieved: 22:54, April 14, 2009, from
  http://question-answering.orgfree.com/
• Cano Esteban, C. (2006). “Sistemas de búsqueda Question-Answering”. In Recuperación y Organización de la Información. Retrieved: 22:58, April 14, 2009, from
  http://usuarios.lycos.es/questionanswering/
• Question answering. (2009, March 20). In Wikipedia, The Free Encyclopedia. Retrieved 19:27, April 13, 2009, from
  http://en.wikipedia.org/w/index.php?title=Question_answering&oldid=278611780

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

]]> http://2jabaste.wordpress.com/2009/04/14/questionnaire-3-question-answering-qa/feed/ 0 2jabaste Question Answering (QA) http://2jabaste.wordpress.com/2009/04/02/questionnaire-2-word-sense-disambiguation-named-entity-recognition/ http://2jabaste.wordpress.com/2009/04/02/questionnaire-2-word-sense-disambiguation-named-entity-recognition/#comments Thu, 02 Apr 2009 18:03:54 +0000 Javier Basterrechea http://2jabaste.wordpress.com/?p=424 ]]>

We have talked in the previous post about Machine Translation (MT) because it is considered a sub-field of the Computational Linguistics that we are currently studying. Broadly speaking, MT consists of translating words or speech from one natural language to another one using translation software. As we said, MT faces two problems in two different sub-areas of Natural Language Processing (NLP) that we are going to discover today in this post.

The Tower of Babel by Pieter Brueghel the Elder. "Come, let us go down and confuse their language so they will not understand each other." (Genesis 11:7)

Word Sense Disambiguation (WSD):

In the first place, and as you all may have guessed by now, Word Sense Disambiguation (WSD) consists of identifying which sense of a word is the most suitable in a given sentence, considering, obviously, that the word has a large number of distinct senses. To achieve this goal there are two different possible approaches: deep approaches and shallow approaches.

Deep approaches presume access to a comprehensive body of world knowledge. Meanwhile, shallow approaches do not try to understand the text; they just consider the surrounding words. To tell the truth, deep approaches have not showed themselves very successful in practice due to the impossibility of having a whole body of knowledge in a computer-readable format; so, shallow approaches are much more used today even though theoretically are not as powerful as deep approaches. However, thanks to the researcher’s work the Word Sense Disambiguation (WSD) is becoming more accurate each time.

Named Entity Recognition (NER):

In the second place, we have to higlight the importance of the Named Entity Recognition (NER), a subtask of information extraction also known as Entity Identification or Entity Extraction. The main aim of NER systems is classifying the elements of texts into predefined categories such as organizations, names of persons, expressions of times, locations, percentages, quatities, monetary values, etc.

There are two main types of NER systems. The ones that work using linguistic grammar-based techniques, and the ones that use statistical models. In practice, the hand-crafted grammar-based systems have demonstrated to be more efficient, obtaining better results. Actually, state-of-the-art NER systems, with an average score around 94%, produce near-human performance. However, they require the support of well-trained computational linguistics, which makes them less competitive in terms of costs.

References:

• Named entity recognition. (2009, March 25). In Wikipedia, The Free Encyclopedia. Retrieved 17:25, March 29, 2009, from
  http://en.wikipedia.org/w/index.php?title=Named_entity_recognition&oldid=279521544
• Agirre, E. & Edmonds, P. (2007). Word Sense Disambiguation: Algorithms and Applications. Springer. Retrieved: 18:56, April 2, 2009, from
  http://www.wsdbook.org/
• Word sense disambiguation. (2009, April 2). In Wikipedia, The Free Encyclopedia. Retrieved 19:10, April 2, 2009, from
  http://en.wikipedia.org/wiki/Word_sense_disambiguation
• Named entity recognition. (2005). In Centrum Voor Nederlandse Taal En Spraak. Retrieved: 18:47, April 1, 2009, from
  http://www.cnts.ua.ac.be/conll2002/ner/

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

Now, we have a clear idea of what Natural Language Processing (NLP) is; besides, we have already mentioned the most common tasks that have to do with NLP. In this post, what I would like to do is to start explaining briefly some of those topics for you to have a better understanding of them.

Trans·la·tion

Machine Translation (MT):

Machine Translation (MT) is a sub-field of Computational Linguistics whose main purpose is to develop computer software in order to translate automatically text or speech from one natural language to another one. At a first approach, MT substitutes words in one natural language for words in the target language. However, with the use of several corpus techniques we can obtain more complex translations.

The translation process could be stated as a two-steps operation. Firstly, the translator must decode the meaning of the source text; secondly, it must re-encode the original meaning in the target language. Explained in this simple way it may sound as a simple process, but in order to achieve successfully this task the translator must be able to interpret and analyse carefully the whole text; for this reason the machine needs to have in-depth knowledge of the grammar, semantics, syntax, idioms… of both the original and the target languages.

MT software has improved dramatically in the last years and, nowadays, it enables users for customization by profession, for instance, which makes the scope of allowable substitutions more limited. Consequently, we are progressively obtaining better results each time. However, MT faces two major problems: word sense disambiguation and named entities recognition. However, I will talk about them soon.

Summing up, and as I have mentioned above, MT consists of translating texts or speech from one natural language to another one using translation software. I wanted to present this topic here becuase I find it fascinating and I think that it will gain much more importance in an early future. And, of course, if you want to know more about it feel free to investigate. You will not regret!

References:

• Machine translation. (2009, March 14). In Wikipedia, The Free Encyclopedia. Retrieved 23:32, March 14, 2009, from
  http://en.wikipedia.org/w/index.php?title=Machine_translation&oldid=277284040
• Machine translation. (2008, December 13). In Artificial Intelligence Topics. Retrieved: 20:56, March 15, 2009, from
  http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/MachineTranslation
• Machine translation. (2009). In European Association for Machine translation. Retrieved: 20:58, March 15, 2009, from
  http://www.eamt.org/mt.php

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

]]> http://2jabaste.wordpress.com/2009/03/15/questionnaire-2-machine-translation-mt/feed/ 0 2jabaste Trans·la·tion http://2jabaste.wordpress.com/2009/03/11/questionnaire-2-classic-hlt-topics/ http://2jabaste.wordpress.com/2009/03/11/questionnaire-2-classic-hlt-topics/#comments Wed, 11 Mar 2009 15:55:25 +0000 Javier Basterrechea http://2jabaste.wordpress.com/?p=405 ]]>

Those of you who are hooked on Human Language Technologies (HLT) will already  know that along the year researchers publish books, give lectures and conferences… about different topics that have to do with this area of Computational Linguistics that we are currently studying.

"Oh, mother! Your databases are *so* 20th century!" "I just don't understand you unstructured kids!"

For that reason I have decided to bring here some of the most classic or important topics discussed in the field of Natural Language Processing (NLP). Here come the examples:

• Spoken language processing;
• Word segmentation, tagging, and chunking;
• Syntax and parsing;
• Lexical semantics and word sense disambiguation;
• Pragmatics, discourse, and dialogue;
• Generation and summarization;
• Named entity recognition;
• Information retrieval, question answering, and information extraction;
• Machine translation and multilingual systems;
• Language resources and tools;
• Linguistic, psychological, and mathematical models of language;
• Machine learning and natural language understanding.

These could be considered the most important tasks in the field of Natural Language Processing (NLP). Consequently, these topics are constantly being mentioned and, of course, investigated by researchers. I am sure that in no time you all will be familiar with them!

References:

• Topics of Interest. (2009). European Chapter of the Association for Computational Linguistics. Retrieved: 17:52, March 11, 2009, from
  http://www.eacl2009.gr/conference/topics
• Natural language processing. (2009, February 19). In Wikipedia, The Free Encyclopedia. Retrieved: 06:25, March 1, 2009, from
  http://en.wikipedia.org/w/index.php?title=Natural_language_processing&oldid=110067975

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

We continue showing you examples of research centres for Human Language Technologies (HLT); today we travel to the Netherlands in order to discover The Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam (Universiteit van Amsterdam).

University of Amsterdam logo

The ILLC , with researchers from the Faculty of Sciences and the Faculty of Humanities, started off in 1986 as the Institute for Logic, Language and Information; but it was not until 1991 that it was officially established as a University Research Institute. Since that year, several faculties and groups such as The Applied Logic Lab have joined the ILLC.

ILLC’s main research area is the study of fundamental principles of encoding, transmission and comprehension of information. Natural and formal languages are, obviously, emphasized, but other information carriers, such as images and music, are studied as well.

As they announce in their website, research at ILLC is “interdisciplinary”. In other words, researchers make an effort in order to bring together insights from various disciplines concerned with information processing such as logic, mathematics, computer science, linguistics, cognitive science, artificial intelligence and philosophy.

"Universiteit van Amsterdam" at night

As we can see the Institute for Logic, Language and Computation (ILLC) at the University of Amsterdam (Universiteit van Amsterdam) is one of the most important research centres for Human Language Technologies (HLT) and that is the reason why we want you to know it. If you want further information (staff, upcoming events, conferences, publications, programmes… ) please click on the references below to find more details.

References:

• University of Amsterdam’s website (2009). (English version) Retrieved: 17:32, March 8, 2009, from
  http://www.uva.nl/start.cfm/la=en
• Institute for Logic, Language and Computation (ILLC) of the University of Amsterdam (UvA) (2009). Retrieved: 17: 34, March 8, 2009, from
  http://www.illc.uva.nl/

(Note: in the future this article could be modified several times in order to include more up-to-date information.)

If you have been following my blog lately, you will already know what Human Language Technologies (HLT) are, as well as some of the most relevant figures that are currently investigating this field. In this post what I would like to do is to start showing you some examples of the best research centres that we have today.

Main entrance to the DFKI

The first centre that we are going to mention due to its great importance is the German Research Centre for Artificial Intelligence (DFKI GmbH). The DFKI, Germany’s leading research centre in the area of innovative software technology, has facilities in Kaiserslautern, Saarbrücken, Bremen, and a project office in Berlin. It has been recognized as a remarkable “centre of excellence” since its foundation as a nonprofit organization in 1988 by several renowned German IT companies and two research facilities. Nowadays, with an overall annual budget of more than € 25 million, the DFKI works hard in its “Living Labs” in order to launch innovative and technological solutions and products for its customers.

But the most interesting division of the DFKI for us is the Language Technology Lab (Saarbrücken), whose mission, as it is announced in its website, is “the improvement of language technology through novel computational techniques for processing text, speech and knowledge, as well as the development of a deeper understanding of human language and thought”.

Monument at the DFKI

The Language Technology Lab,  headed by Hans Uszkoreit, is currently working, for example, in these projects: automatically associating recognized information with related information and thus building up collective knowledge, automatically structuring and visualizing extracted information, processing information encoded in multiple languages.

In addition, the DFKI divulges regularly a whole series of publications about Human Language Technology topics. Taking all this into account we realize how important this research centre is in Germany, and, by extension, in Europe.

References:

• German Research Centre for Artificial Intelligence website (2009). (English version) Retrieved: 21:42, March 7, 2009, from
  http://www.dfki.de/web
• German Research Centre for Artificial Intelligence. “Language Technology Lab” website (2009). (English version) Retrieved: 21:44, March 7, 2009, from
  http://www.dfki.de/lt/

(Note: in the future this article could be modified several times in order to include more up-to-date information.)