Literaturan, beti egon izan dira amodiozko istorioak, maitasun hori gauzatzeko oztopoak bezala. Honen adibide ditugu historian zehar idatzi diren milaka eta milaka nobela:  No diga que fue un sueño, Las desventuras del joven Werther, Perdona si te llamo amor, Libro de amor,  El beso, Romeo y Julieta, La Celestina…

Honekin batera, aipatu beharrekoak dira hurrengo amodiozko film hauek: Romeo y Julita, Los puentes de Madison, Un paseo por la nubes, Oficial y caballero, El hombre que susurraba a los caballos, Tienes un e-mail, El diario de Noa, Tristán e Isolda, Un día inesperado, Titanic, Realmente amor, Diez cosas que odio de ti, Como si fuera cierto, Moulin Rouge, Pretty woman…

Gu, orain, Las desventuras del joven Werther nobelan eta El diario de Noa pelikulan oinarrituko gara:

Werther 1774an idatzi zen. Protagonista Wahlheim izeneko herri fiktizio baten bizi da. Bertan, Lotte izeneko neskatxa eder batez maitemintzen da, nahiz eta hasieratik jakin maitasun hori ezinezkoa dela, Adolfo izeneko ezkongaia baitu.

El diario de Noa, aldiz, 1940ko hamarkadan girotuta dago. Noa hiritik urrun dagoen herrixka baten bizi den mutil apal bat da; Ally, aldiz, oporretan bertara doan famili aberats bateko alaba. Uda elkarrekin pasatu ondoren, Ally-ren gurasoak bertatik urruntzea erabakitzen dute, Ally eta Noa-ren artean sortu den maitasuna apurtu nahirik.

Bi istorio hauek gauza asko dituzte amankomunean:

• Eskutitzak. Bai Werther-ek eta bai Noa-k beraien sentimenduak eskutitzen bitartez adierazten dituzte, nahiz eta lehenengoak lagun bati bidali eta bigarrenak Ally-ri.

• Bi gizonek dute norbait bere sentimenduei buruz hitz egin eta lasai geratzeko: Werther-ek Wilhem eta Noa-k bere betiko laguna, beranduago gudan hilko dena.

• Gizarteak ezarritako oztopoak. Werther-ek duen traba Lotte emakume ezkondua dela da, eta garaiko gizarteak ez zuen onartzen ezkondutako emakumeengana hurbiltzea. Noa-ren kasuan, gizartearen aurreiritziek bikoteari kalte egiten diote; hau da, Ally-ren gurasoek ez dute Noa onartzen bere maila ekonomikoa dela eta. Horregatik, herrixkatik alde egitea erabakitzen dute. Ikusten dugun bezala, kasu honetan balio moralen ondorioa den gurasoen jarrera da oztoporik handiena. Ally-ren gurasoek bere alabarentzat etorkizun oparo bat besterik ez dute nahi, senar on bat izatea, bizi maila on bat emateko gai izango dena.

• Bai Werther-ek eta bai Noa-k hausnarketarako toki bat dute. Werther-ek naturan aurkitzen du lasaitasuna, eta Noa-k ahatez beteriko laku baten.

• Zorakeria. Bi gizonek, sentitzen duten maitasuna dela eta, burua galtzen dute. Werther-en zoramenak hain dimentsio handia hartzen du non bere buruaz beste egiten bukatzen duen; eta Noa ere, bere etxea eraikitzen hasten denean, jendeak zorotzat hartzen du.

• Bi protagonistek beraien maitaleei olerkiak irakurtzen dizkie: Werther-ek Ossian-enak, eta Noa-k bere aitak txikitan irakurrarazten zizkionak bere totaldura zuzentzeko.

• Kasu bietan, neskak dira oztopo handienak jartzen dituztenak, beraiek baitira beste gizon batekin konprometituta daudenak.

• Bi gizonentzako, oroitzapenak oso garrantzitsuak dira, iraganean gertatutako gauzei atxikitzen dira. Werther-entzat, esaterako, Lotte ezagutu zuen eguneko arropak oso bereziak dira; eta Noa-k, Ally-rekin gau bat pasatu zueneko etxea (zein jausteko zorian dagoen) du beti gogoan.

• Werther-ek eta Noa-k oso argi ikusten dute Lotte eta Ally direla beraien bizitzako emakumeak, eta beraiengatik edozer egiteko gai dira. Hau oso argi ikusten da Werther-ek bere buruaz beste egiten duenean, ekintza hau berarentzat maitasunaren adierazle baita. Noak, bestalde, bere zahartzaroan dena alde batera uzten du Ally zaintzeko zaharren egoitza baten dagoenean.

• Ally-k eta Lotte-k, biek jotzen dute pianoa. Moduren batean, lasaitzeko erabiltzen zuten medio bat da.

Desberdintasunei dagokienez, hurrengoak aipatuko ditugu:

• Werther Erromantizismo garaiko ezaugarriekin bat dator. Lar sentibera da, oso dramatikoa. Bere sentimenduak bapigakatzen ditu. Noak, aldiz, iniziatiba gehiago du, nahi duena lortzeko borrokatzen du. Hau kontutan hartuz, Werther Erromantizismoan tipikoa zen heroi mota batekin identifikatzen dugu: heroi barnerakoiarekin. Hau egoeraren biktima bihurtzen da. Honekin kontrastatuz, Noa-k Lord Byron-en Don Juan-engan pentsarazten digu. Nahiz eta heroi mota honen ezaugarri guztiak ez bete, antzekotasun gehiago dituela esan dezakegu, gizarteak ezarritako trabak gainditzen saiatzen baita.

• Lotte eta Ally-ren izaera ere aipagarria da, oso desberdinak baitira. Biak dira adeitsuak, eta beraietaz espero dena egiten dute; baina Ally-k azkenean maitasuna beste gauza guztien gainetik jartzen du. Horrez gain, Ally ausartagoa da, Luke-ekin ezkontzekotan dagoenean, Noa-renganako sentitzen duen maitasuna adierazten du.

• Bi gizonen jarrera. Werther oso pesimista da, Noak berriz, bizitza beste era batera ikusten du. Honen adierazle dugu Noa-ren jarrera Ally-ren gaixotasuna dela eta. Nahiz eta medikuek gehiago ez saiatzeko esan, berak ez du itxaropena galtzen, eta mirarietan sinesten du.

• Amaiera.Werther-ek bere buruaz beste egiten duela aipatudugu. Honen arrazoia bizi duen egoerak irabazten duela da. Bi gizonetako bat soberan dago, eta arazo horri aurre egiteko erarik azkarrena eta errazena errenditzea da, ez baitu inolako esperantzarik. Noa, aldiz, nahi duena lortzeko edozer egiteko gai da, eta bere helburua lortu arte borrokatu egingo du. Honela bada,  nahiz eta Werther eta Lotte-rekin berdin gertatu ez, Noa eta Ally-ren erlazioak amaiera ona izango du.

Beraz, ikus dezakegu  bai denborak eta bai lekuak berdin diotela, maitasuna denon baitan dagoen zerbait da.  Egoerak askotan diruditenak baino antzekoagoak dira eta askotan desberdintasun bat markatuko duen gauza bakarra norberaren jarrera izango da, norberak maitasun horrengatik borrokatzeko duen gogoa.

1. Grammatic errors: as we can see in the sentence La construcción de torre a Babel es una narración bíblica conocida del Testamento Viejo, there is a grammatical error. In Spanish, construcción de must be followed by a noun and its article. As we can see, this is just one of the thounsands of differences that must be between German and Spanish.
2. Nonsense sentences: each language has its own order of sentences, and these two languages have no similarities in this aspect, as we can see in Consideran el acontecimiento|evento de la confusión de idioma sin embargo la mayor parte no para histórico, sino para mític. The sentence is nor coherent neither logical. The elements are in a wrong place in the sentence, and it is clear that the machine has translated it literally.
3. Genre errors: Desde 1913 está ocupado por hallazgos arqueológicos que la historia se refiera a una torre histórica. This sentence refers to the Babel tower, which is feminine in Spanish. In German it is not, and the MT has translated it literally once again.
4. Lexic errors: La biblia habla de un pueblo del este que habla un idioma y se establece en el nivel|llanura en un país con nombre de Schin. In this case, the authomatic translator does not know which term to choose. Depending on the word, the sentence can completely lose its sense, so it is essential to use the correct term in order to build a correct sentence.
5. Spelling errors: Ahora teme que no les parece “nada más de forma inalcanzable” qué se pueden proponer también, o como se modificaría hoy quizá que el pueblo se podría hacer megalómano. Here, apart from the incorrect use of the verbs – in Spanish the verb temer is followed by subjunctive – the spelling is incorrect. We must make a difference between que/qué and como/cómo, since they have quite different meanings.

As a conclusion, we can mention that MTs may not be useful to translate texts written in a languages that are very different from ours. They may be used for little translations, but that is all.

Sources:

• Torre de Babel (Deutsch). (2008). Comprendum Translator. Retrieved: 14:36, April 23, from http://www.translendium.net:8080/home/

• Torre de Babel (Deutsch). (2008). In Wikipedia, The Free Encyclopedia. Retrieved: 13:51, April 21, from http://de.wikipedia.org/wiki/Turmbau_zu_Babel

• Torre de Babel (Español). (2008). Comprendum Translator. Retrieved: 13:57, April 21, from http://www.translendium.net:8080/home/url.do;jsessionid=CAA572C7E1E362D3E3D2

1. Agreement errors: If we translate a text from Wikipedia (Catalonian-Spanish), we can find errors like this: “La torre de Babel fue, según la Bíblia^[1], un gran proyecto de los hombres para construir una ciudad en vertical en Babel, la tierra de xinar que emprendieron a los hombres después del gran diluvio universal”. The sentence is incorrect. It should be (…) “que emprendieron los hombres”.
2. Spelling errors: in the sentence “Babel viene de la palabra Accadi Bb-ili (La puerta de Dios), donde|dónde Bb quiere decir puerta y il dios” we can see that the authomatic translator is not able to make a difference between donde and dónde. In plus, we must consider that this punctuation mistake can change completely the sense of the sentence.
3. Grammar errors: “Estos hombres eran pues, los descendientes de Noè y representaban la humanidad hablando de una única lengua” shows a very clear example of the differences between these two languages. In Spanish, hablando de refers to the topic someone has been talking about. If we ant to specify something, we do not put a preposition.
4. Vocabulary errors: “Como el corazón|coro de los hombres era desobediente y malvado, y en vez de adorar a Dios se preocupaban sólo por ellos mismos, Dios, con el fin de frustar este proyecto, multiplicó el número de (…)”. In this case, there is a severe vocabulary mistake. In Spanish, the expression “frustrar un proyecto” does not exist. The tranbslator should consider other meanings of the word.
5. Punctuation errors: “multiplicó el número de lenguas porque así no se podrían comprender entre ellos y por lo tanto, no tener que detener la construcción de la ciudad”. This sentence has a punctuation mistake. Adverbs like “por lo tanto” must be placed between two commas.

As we can see, an authomatic translator machine can be quite useful in some occasions, but we can not trust them if we want to have a completely correct and proper translation. We must not forget that each language has its characteristics, no matter how similar they are, as happens in the case of Spanish and Catalonian.

Sources:

• Torre de Babel (Catalá). (2008). In Wikipedia, The Free Encyclopedia. Retrieved: 13:51, April 21, from http://ca.wikipedia.org/wiki/Torre_de_Babel
• Torre de Babel (Español). (2008). Comprendum Translator. Retrieved: 13:57, April 21, from http://www.translendium.net:8080/home/url.do;jsessionid=CAA572C7E1E362D3E3D2952A2113BCC7
• Torre de Babel (Catalá). (2008). Comprendum Translator. Retrieved: 21:57, April 21, from  http://www.translendium.net:8080/home/

• Machine Translation: according to the Wikipedia, 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. 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. Improved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are names. With the assistance 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”. However, current systems are unable to produce output of the same quality as a human translator, particularly where the text to be translated uses casual language.
• Computer-assisted 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 sometimes called machine-assisted, or machine-aided, 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.
• 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 language locales – does. Deciding where multiple language versions of content are going to be required and where content can be maintained separately for different locales is a critical decision that will affect how a site should be maintained and what it will cost.
• Translation technology: 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.”

Sources:

• Machine Translation. (2008, May 5). In Wikipedia, The Free Encyclopedia. Retrieved: 11:03, April 15, 2008 from http://en.wikipedia.org/wiki/Machine_translation

• Computer-assited Translation. (2008, May 2). In Wikipedia, The Free Encyclopedia. Retrieved: 15:11, April 15, 2008 from http://en.wikipedia.org/wiki/Computer-assisted_translation

• Multilingual Conrtent anagement. In Kitside. Retrieved: 22:59, April 15, 2008 from http://www.kitsite.com/articles/multilingual-content-management.html

• Translation Technology. (2008, May 4). In Wikipedia, The Free Encyclopedia. Retrieved: 22:44, April 15, 2008 from http://en.wikipedia.org/wiki/Translation_technology

1. One of them lists the possible characteristics of the contexts of use that can be applied to MT systems.
2. The other one lists the possible characteristics of an MT system, along with the metrics that were proposed to measure them.

FEMTI is used by many evaluators every day, so it proposes some characteristics of high quality that are relevant to that context, using its embedded knowledge base. Evaluators can modify this set of characteristics and choose an evaluation metric for each of them by browsing the second classification. Then, evaluators can print the evaluation plan and execute the it.

According to the FEMTI report, a translation task has specific characteristics. This refers to the information flow intended for the output, from the point of view of the agent (human or otherwise) who receives the translation. As J.C. Sager noted for Machine Translation systems, two types of use [are] to be considered: (a) the un-edited output; (b) the edited output. The output may be acceptable for either use or both and the evaluation should determine this. In the case of edited output the cost of revision, editing etc. has to be established and compared with the cost of manual translation. Since the type of use is related to the type of text, these types have to be established and taken into account.

In the other hand, In Toward Finely Differentiated Evaluation Metrics for Machine Translation, Hovy suggests dividing all the possible translation tasks into three main groups. He saidthat in order to make the taxonomization of features useful to people who do not already know much about MT and do not wish to become experts in evaluation, it is important to articulate its layers and choices in terms they can intuitively understand. This part of the present evaluation taxonomy describes three principal types of use in such a way that users can identify the particular type of work they want to have done, while developers can define in strict terms what their MT system can do.

Apart from that, the main characteristics of a translation task according to the FEMTI report are the following:

• Assimilation: its purpose is to monitor a relatively large volume of texts produced by people outside the organization, in several languages. This characteristic includes document routing / sorting, information extraction or summarization and a search process.
• Dissemination: it pretends to deliver to others a translation of documents produced inside the organization. Internal dissemination (routine internal dissemination and experimental internal dissemination) and external dissemination-publication (single client external dissemination and multi-client external dissemination) are included in here.
• Communication: its aim is to support multi-turn dialogues between people who speak different languages. The translation quality must be high enough for good and fluent conversation. The ultimate purpose of dissemination is to deliver to others a translation of documents produced inside the organization. Synchronous or interactive communication and synchronous or delayed communication must be mentioned at this point too.

Sources:

• Eduard Hovy, Margaret King & Andrei Popescu-Belis. (2003). FEMTI – Framework for the Evaluation of Machine Translation in ISLE. Retrieved April 9, 2008, from http://www.issco.unige.ch:8080/cocoon/femti/st-home.html

• Computational Semantics: it consists of extracting the meaning representations from texts, ranging from shallow representations like named entities and thematic roles and to deepen structures like quantification in logic. Among othedr things, it includes Named Entity Recognition (NER) and Information Extraction (IE). This group worked in a wide range of NER and IE related issues and used a Maximum Entropy Markov Model. In 2003 they wanted thatBiomedical papers domined NER, which required identifying genes and proteins but not making a difference between the two.
• Parsing and Tagging: this consists of assigning part of speech and syntactic structure by algorithms, emphasizing probabilistic and discriminative models. It includes Probabilistic Parsing and Part-of-speech Tagging, which assigns the correct part of speech (verb, adjective, noun etc.) to words. This group has worked on building probabilistic conditional long-linear models for tagging. This tagger can be considered as the best for English uses.
• Multilingual NLP: this is a variety of NLP investigations in Chinese, Arabic and German. It includes tagging, segmentation, probabilistic syntactic parsing, and semantic role parsing. Inside this froup, we must make a difference between Arabic, German and Chinese NLP. The Chinese one does research in Chinese Natural Language Processing and includes word segmentation, part-of-speech tagging, sintactic and semantic parsing and so on.
• Unsupervised Induction of Linguistic Structure: this section analyses probabilistic and other corpus-based algorithms with the aim of learning syntactic, morphological and phonological structures from corpora. This research topic includes grammar induction – ‘the more linguistic structure that can be learned, the less need there is for large marked-up corpora -, Semantic Taxonomy Induction – focused on acquiring the basic semantic relationships between words from corpora- and Morphology and Fonology Induction.

Sources:

• The Standford NLP Group. Retrieved: 17:04, 1 April, 2008 from http://nlp.stanford.edu/research.shtml

• Linguistic of the corpus.
• Authomatic translation.
• Authomatic analysis of the textual context.
• Linguistic, mathematical and psicolinguistic models of the language.
• Monolingual and multilingual textual generation.

hvhvjhjkbhkghygkjbkjhuihghgghvhkihgiyugtyfghgvhjgygyugtfhgihjhuiyyttghuiyhuiyygyugyuyggyuguygu On the other hand, the DFKI-LT Projects include other fields in their research. For instance, they deal with themes such as exploiting, tighter integration of shallow and deep techniques in processing, enriching deep processing with statistical methods, they combine language checking with structuring tools in document authoring, document indexing for German and English, automatically associating recognized information with related information, automatically structuring and visualizingprocessing information encoded in multiple language extracted information and s, among them Chinese and Japanese.

Finally, I am going to mention another research centre which does research in some other areas. I talk about IxA NPL Research Group, from the University of the Basque Country. This group investigates the syntax, the lexicon and the morpho making use different tools such as tools for creating and prcessing corpora and a lexicographer workbench.

Sources:

• Ixa Group Natural Language Processing. University of the Basque Country. Retrieved: 13:37, March 31, 2008, from http://ixa.si.ehu.es/Ixa/Ikerlerroak/
• ELSNET. European Network of Excellence in Human Language Technologies (2008, February 13). Retrieved: 13:40, March 31, 2008, from http://www.elsnet.org/
• The Stanford Natural Language Processing Group. Retrieved: 12:52, March 31, 2008, from http://nlp.stanford.edu/
• XXIV Congreso SEPLIN 2008. Retrieved: 16:30, April 1, 2008, from http://basesdatos.uc3m.es/sepln2008/web/index.html

• Avgustinova, T. (1994): On Bulgarian Verbal Critics. Journal of Slavic Linguistics, 2:1. Also available as CLAUS Report 33.
• Avgustinova, T. (1998): Determinedness and Replication Potential of Nominal Materia in Bulgarian. Balkanistica (ed. by D.L.Dyer), Vol. 11, Seiten: 1-17.
• Avgustinova, T. and W. Skut (1997): Panslavism Revisited. Presented at the 30th Poznan Linguistic Meeting, Workshop on Slavic Languages in HPSG, Poznan.As I said, Uszkoreit worked in this world of human technologies.

As well as him, many other people work at research centers for human language technologies. We can find some centers in Europe, and the followings are an example of it:

• National Center for Language Technology: located in Dublin, the centre “carries out basic research and develops applications”. They believe that language is the key modality in communication. As it says in its page, it conducts research into the processing of human language by computers, such as speech recognition and synthesis, machine translation, human-computer interfaces, information retrieval and extraction, the teaching and learning of languages using computers and software localisation and globalisation. HLT also has substantial economic implications and potential. The centre carries out basic research and develops applications.
• OFAI Language Technology Group: it is an Austrian centre that “conduct research in modelling and processing human languages, especially ofr German”. Language Technology (LT) forms a major research area at this Austrian centre. conduct research in modelling and processing human languages, especially for German. This includes constructing linguistic resources (such as lexicons, grammars, discourse models), processing algorithms (such as morphological components, parsers, generators, speech synthesizers, discourse processing components), and application prototypes (such as natural language interfaces, advisory systems and concept-to-speech systems). The Language Technology Group at OFAI is a member of the EU’s European Network of Excellence in Human Language Technologies (ELSNET)
• Edinburgh Language Technology Group (LTG): they focus on “building practical solutions to real problems in text processing”, and it is a “research and development group that has been working in the area of natural language engineering since the early 1990s”. The LTG’s work is oriented to application: “we focus on building practical solutions to real problems in text processing. We have worked in all areas of large-volume text handling, from text annotation through markup architectures and from information extraction to automatic or computer-assisted generation of text”.

Sources:

• OFAI Language Technology Group. (2007, May 16, 15:59). LT Group. Retrieved: 13:27, March 17, 2008, from http://www.ofai.at/research/nlu/
• Edinburgh Language Technology Group. (2006, June 30, 16:53). Retrieved: 13:36, March 17, 2008, from http://www.ltg.ed.ac.uk/
• National Center for Language Technology (October 6,2007). NCLT. Retrieved: 19:24, March 7, 2008, from http://www.nclt.dcu.ie/
• Hans Uszkoreit. Recent Publications. Retrieved: 15:12, March 3, 2008, from http://www.coli.uni-saarland.de/~hansu/

Uszkoreit studied linguistics and Computer Science at the Technical University of Berlin and the University of Texas at Austin. During his time in this last place, he worked as a research associate in a large machine translation project at the Linguistics Research Center. In 1984 Uszkoreit received the Ph.D. in linguistics. He also worked as a computer scientist at the Artificial Intelligence Center of SRI International in Menlo Park (1982-1986). During this time he affiliated with the Center for the Studyof Language and Information at Standford University, and became a senior researcher and a project leader. He stayed in Stuttgart for six months, on a IBM Research Fellowship at the Science Division of IBM Germany. In December 1986 he returned to Stuttgart to work for IBM Germany as a project leader in LILOG (Linguistic and Logical Methods for the Understanding of German Texts. During all this time, he taught at the University of Stuttgart too.

Respecting to his publications, he has made lots of works. An example of these are the following works:

• Uszkoreit, H. (2007) Methods and Applications for Relation Detection. In: Proceedings of the Third IEEE International Conference on Natural Language Procesing and Knowledge Engineering, Beijing, 2007. Xu, F. ,
• H. Uszkoreit and H. Li (2006) Automatic Event and Relation Detection with Seeds of Varying Complexity, To appear, To appear in: Proceedings of the AAAI 2006 Workshop Event Extraction and Synthesis, Boston, July, 2006. Buseman, S. and H. Uszkoreit (2004) Predicting the Future: Technology Roadmapping. In: ELSNews, 2004.
• Uszkoreit, H.; B. Jörg anf G. Erbach (2003) An Ontology-based Knowledge Portal for Language Technology. In: Proceedings of ENABLER/ELSNET Workshop “International Roadmap for Language Resources”, Paris.
• Uszkoreit, H. (2002) New Chances for Deep Linguistic Processing, In: Procedings od COLING 2002, Taipei.
• Avgustinova, T., H. Uszkoreit (2001) Towards a typology of agreement phenomena. In: The Role of Agreement in Natural Language: TLS 5 Proceedings. Austin, TX, Texas Linguistics Forum 53 (2003), 167-180.

Sources:

• European Research for Human Language Technologies. WordPress. Retrieved: 17:01, March 1, 2008 from http://lamala.wordpress.com/2007/04/17/european-research-centres-for-human-language-technologies/
• Hans Uzkoreit. Homepage. (January 24, 2008). Retrieved: 16:27 , March 1, 2008 from http://www.coli.uni-saarland.de/~hansu/
• Wikipedia. The Free Encyclopedia . Natural Language Processing. Retrieved: 16:31, March 1, 2008 from http://en.wikipedia.org/w/index.php?title=Natural_language_processing&oldid=110067975
• Language Technology Lab. What is language technology? Hans Uszkoreit (2008). Retrieved: 18:07, 1 March, 2008 from http://www.dfki.de/lt/lt-general.php

Beste alde batetik, XML-k dituen teknologien artean, hurrengoak aurki daitezke:

• XSL: eXtensive Stylesheet Language. Adituen arabera, edukiaren antolaketa zehaztea, diseinuaren nondik norakoak erabakitzea eta honen aurkezpena antolatzea dira bere egitekoak.
• XPath: XML-ko dokumentu batera iristeko erabiltzen den hizkuntza da. Gainera, balore desberdinak XML formatura pasatzeko ere erabil daiteke. Wikipediaren arabera, XPath-aren hizkuntza XML dokumentuaren zuhaitz erako irudikapen baten oinarritzen da; zuhaitz horretan nabigatzeko aukera ere eskeintzen duelarik.
• XLink: XML loturaren hizkuntza. XML formatuko dokumentuekan elementuak sartzea ahalbidetzen duen hizkuntza da, XML-ren balibideen artean loturak sortzeko.
• XPointer: Interneten oinarritutako osagaiei erreferentzia egiteko erabiltzen da. Wikipediak dioenez, XPointer-a hurrengo espezifikazioetan sailka dezkegu: framework bat, XML formatuan dauden oinarriak identifikatzen dituena; proiektuarengana zuzentzen den elementu posizionatzailea eta namespeaces deituriko sistementzako proiektu bat.
• XQL: Structured Query Language. XML kontsultatzeko hizkuntza da. XML dokumentuetatik datuen erauzketa errazten duen hizkuntza da, eta sarean XML formatuan baita ere dauden datuak ateratzeko ere balio du.hhhhhhhhhhhhffffffffffffffffffffffffffffffffgggggggggggggggggggggggggfffffhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh

Ikus daitekenez, XML teknologiaren munduan sartuta dagoen beste mundu bat da. Sistema honek eskeintzen dizkigun aukerek eta baliabideek amaigabeak dirudite. Formatu honekin, datuen antolaketak, aurkezpenak eta elkartrukeak edozein pertsonen artean egin daitezkela esan daiteke.

Bibliografia:

• XML. (2008, 5 de febrero). Wikipedia, La enciclopedia libre. Fecha de consulta: 13:42, febrero 5, 2008; http://www.w3c.es/divulgacion/guiasbreves/TecnologiasXML
• XPath. (2008, 5 de febrero). Wikipedia, La enciclopedia libre. Fecha de consulta: 13:55, febrero 5, 2008; http://en.wikipedia.org/wiki/XPath
• XPointer. (2008, 5 de febrero). Wikipedia, La enciclopedia libre. Fecha de consulta: 14:505, febrero 5, 2008; http://en.wikipedia.org/wiki/XPointer
• SQL. (2008, 5 de febrero). Wikipedia, La enciclopedia libre. Fecha de consulta: 14:11, febrero 5, 2008; http://en.wikipedia.org/wiki/SQL