CHARACTER-LEVEL INTERACTIONS IN MULTIMODAL COMPUTER-ASSISTED TRANSCRIPTION OF TEXT IMAGES

HTR systems don't achieve acceptable results in unconstrained applications. Therefore, it is convenient to use a system that allows the user to cooperate in the most confortable way with the system to generate a correct transcription. In this paper, multimodal interaction at character-level is studied.Martín-Albo Simón, D. (2011). CHARACTER-LEVEL INTERACTIONS IN MULTIMODAL COMPUTER-ASSISTED TRANSCRIPTION OF TEXT IMAGES. http://hdl.handle.net/10251/11313Archivo delegad

Automatic extraction of the size of myocardial infarction in an experimental murine model

Tese de mestrado. Engenharia Biomédica. Universidade do Porto. Faculdade de Engenharia. 201

Querying out-of-vocabulary words in lexicon-based keyword spotting

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2197-8[EN] Lexicon-based handwritten text keyword spotting (KWS) has proven to be a faster and more accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS relies on a predefined vocabulary, fixed in the training phase, it does not support queries involving out-of-vocabulary (OOV) keywords. In this paper, we outline previous work aimed at solving this problem and present a new approach based on smoothing the (null) scores of OOV keywords by means of the information provided by ``similar'' in-vocabulary words. Good results achieved using this approach are compared with previously published alternatives on different data sets.This work was partially supported by the Spanish MEC under FPU Grant FPU13/06281, by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMA-MATER, and through the EU Projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).Puigcerver, J.; Toselli, AH.; Vidal, E. (2016). Querying out-of-vocabulary words in lexicon-based keyword spotting. Neural Computing and Applications. 1-10. https://doi.org/10.1007/s00521-016-2197-8S110Almazan J, Gordo A, Fornes A, Valveny E (2013) Handwritten word spotting with corrected attributes. In: 2013 IEEE international conference on computer vision (ICCV), pp 1017–1024. doi: 10.1109/ICCV.2013.130Amengual JC, Vidal E (2000) On the estimation of error-correcting parameters. In: Proceedings 15th international conference on pattern recognition, 2000, vol 2, pp 883–886Fernández D, Lladós J, Fornés A (2011) Handwritten word spotting in old manuscript images using a pseudo-structural descriptor organized in a hash structure. In: Vitri'a J, Sanches JM, Hern'andez M (eds) Pattern recognition and image analysis: Proceedings of 5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8–10. Springer, Berlin, Heidelberg, pp 628–635. doi: 10.1007/978-3-642-21257-4_78Fischer A, Keller A, Frinken V, Bunke H (2012) Lexicon-free handwritten word spotting using character HMMs. Pattern Recognit Lett 33(7):934–942. doi: 10.1016/j.patrec.2011.09.009 Special Issue on Awards from ICPR 2010Fornés A, Frinken V, Fischer A, Almazán J, Jackson G, Bunke H (2011) A keyword spotting approach using blurred shape model-based descriptors. In: Proceedings of the 2011 workshop on historical document imaging and processing, pp 83–90. ACMFrinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE Trans Pattern Anal Mach Intell 34(2):211–224. doi: 10.1109/TPAMI.2011.113Gatos B, Pratikakis I (2009) Segmentation-free word spotting in historical printed documents. In: 10th International conference on document analysis and recognition, 2009. ICDAR’09, pp 271–275. IEEEJelinek F (1998) Statistical methods for speech recognition. MIT Press, CambridgeKneser R, Ney H (1995) Improved backing-off for N-gram language modeling. In: International conference on acoustics, speech and signal processing (ICASSP ’95), vol 1, pp 181–184. IEEE Computer Society, Los Alamitos, CA, USA. doi: http://doi.ieeecomputersociety.org/10.1109/ICASSP.1995.479394Kolcz A, Alspector J, Augusteijn M, Carlson R, Popescu GV (2000) A line-oriented approach to word spotting in handwritten documents. Pattern Anal Appl 3:153–168. doi: 10.1007/s100440070020Konidaris T, Gatos B, Ntzios K, Pratikakis I, Theodoridis S, Perantonis SJ (2007) Keyword-guided word spotting in historical printed documents using synthetic data and user feedback. Int J Doc Anal Recognit 9(2–4):167–177Kumar G, Govindaraju V (2014) Bayesian active learning for keyword spotting in handwritten documents. In: 2014 22nd International conference on pattern recognition (ICPR), pp 2041–2046. IEEELevenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Dokl 10(8):707–710Manning CD, Raghavan P, Schtze H (2008) Introduction to information retrieval. Cambridge University Press, New YorkMarti UV, Bunke H (2002) The IAM-database: an English sentence database for offline handwriting recognition. Int J Doc Anal Recognit 5(1):39–46. doi: 10.1007/s100320200071Puigcerver J, Toselli AH, Vidal E (2014) Word-graph and character-lattice combination for KWS in handwritten documents. In: 14th International conference on frontiers in handwriting recognition (ICFHR), pp 181–186Puigcerver J, Toselli AH, Vidal E (2014) Word-graph-based handwriting keyword spotting of out-of-vocabulary queries. In: 22nd International conference on pattern recognition (ICPR), pp 2035–2040Puigcerver J, Toselli AH, Vidal E (2015) A new smoothing method for lexicon-based handwritten text keyword spotting. In: 7th Iberian conference on pattern recognition and image analysis. SpringerRath T, Manmatha R (2007) Word spotting for historical documents. Int J Doc Anal Recognit 9:139–152Robertson S. (2008) A new interpretation of average precision. In: Proceedings of the international. ACM SIGIR conference on research and development in information retrieval (SIGIR ’08), pp 689–690. ACM, New York, NY, USA. doi: http://doi.acm.org/10.1145/1390334.1390453Rodriguez-Serrano JA, Perronnin F (2009) Handwritten word-spotting using hidden markov models and universal vocabularies. Pattern Recognit 42(9):2106–2116. doi: 10.1016/j.patcog.2009.02.005 . http://www.sciencedirect.com/science/article/pii/S0031320309000673Rusinol M, Aldavert D, Toledo R, Llados J (2011) Browsing heterogeneous document collections by a segmentation-free word spotting method. In: International conference on document analysis and recognition (ICDAR), pp 63–67. doi: 10.1109/ICDAR.2011.22Shang H, Merrettal T (1996) Tries for approximate string matching. IEEE Trans Knowl Data Eng 8(4):540–547Toselli AH, Vidal E (2013) Fast HMM-Filler approach for key word spotting in handwritten documents. In: Proceedings of the 12th international conference on document analysis and recognition (ICDAR), pp 501–505Toselli AH, Vidal E (2014) Word-graph based handwriting key-word spotting: impact of word-graph size on performance. In: 11th IAPR international workshop on document analysis systems (DAS), pp 176–180. IEEEToselli AH, Vidal E, Romero V, Frinken V (2013) Word-graph based keyword spotting and indexing of handwritten document images. Technical report, Universitat Politécnica de ValénciaVidal E, Toselli AH, Puigcerver J (2015) High performance query-by-example keyword spotting using query-by-string techniques. In: 2015 13th International conference on document analysis and recognition (ICDAR), pp 741–745. IEEEWoodland P, Leggetter C, Odell J, Valtchev V, Young S (1995) The 1994 HTK large vocabulary speech recognition system. In: International conference on acoustics, speech, and signal processing (ICASSP ’95), vol 1, pp 73 –76. doi: 10.1109/ICASSP.1995.479276Wshah S, Kumar G, Govindaraju V (2012) Script independent word spotting in offline handwritten documents based on hidden markov models. In: 2012 International conference on frontiers in handwriting recognition (ICFHR), pp 14–19. doi: 10.1109/ICFHR.2012.26

End-to-End Neural Optical Music Recognition of Monophonic Scores

[EN] Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Images of Music Staves (PrIMuS) dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.This work was supported by the Social Sciences and Humanities Research Council of Canada, and the Spanish Ministerio de Economia y Competitividad through Project HISPAMUS Ref. No. TIN2017-86576-R (supported by UE FEDER funds).Calvo-Zaragoza, J.; Rizo, D. (2018). End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied Sciences. 8(4). https://doi.org/10.3390/app8040606S8

Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing

Natural Language Processing (NLP) is an interdisciplinary research field of Computer Science, Linguistics, and Pattern Recognition that studies, among others, the use of human natural languages in Human-Computer Interaction (HCI). Most of NLP research tasks can be applied for solving real-world problems. This is the case of natural language recognition and natural language translation, that can be used for building automatic systems for document transcription and document translation. Regarding digitalised handwritten text documents, transcription is used to obtain an easy digital access to the contents, since simple image digitalisation only provides, in most cases, search by image and not by linguistic contents (keywords, expressions, syntactic or semantic categories). Transcription is even more important in historical manuscripts, since most of these documents are unique and the preservation of their contents is crucial for cultural and historical reasons. The transcription of historical manuscripts is usually done by paleographers, who are experts on ancient script and vocabulary. Recently, Handwritten Text Recognition (HTR) has become a common tool for assisting paleographers in their task, by providing a draft transcription that they may amend with more or less sophisticated methods. This draft transcription is useful when it presents an error rate low enough to make the amending process more comfortable than a complete transcription from scratch. Thus, obtaining a draft transcription with an acceptable low error rate is crucial to have this NLP technology incorporated into the transcription process. The work described in this thesis is focused on the improvement of the draft transcription offered by an HTR system, with the aim of reducing the effort made by paleographers for obtaining the actual transcription on digitalised historical manuscripts. This problem is faced from three different, but complementary, scenarios: · Multimodality: The use of HTR systems allow paleographers to speed up the manual transcription process, since they are able to correct on a draft transcription. Another alternative is to obtain the draft transcription by dictating the contents to an Automatic Speech Recognition (ASR) system. When both sources (image and speech) are available, a multimodal combination is possible and an iterative process can be used in order to refine the final hypothesis. · Interactivity: The use of assistive technologies in the transcription process allows one to reduce the time and human effort required for obtaining the actual transcription, given that the assistive system and the palaeographer cooperate to generate a perfect transcription. Multimodal feedback can be used to provide the assistive system with additional sources of information by using signals that represent the whole same sequence of words to transcribe (e.g. a text image, and the speech of the dictation of the contents of this text image), or that represent just a word or character to correct (e.g. an on-line handwritten word). · Crowdsourcing: Open distributed collaboration emerges as a powerful tool for massive transcription at a relatively low cost, since the paleographer supervision effort may be dramatically reduced. Multimodal combination allows one to use the speech dictation of handwritten text lines in a multimodal crowdsourcing platform, where collaborators may provide their speech by using their own mobile device instead of using desktop or laptop computers, which makes it possible to recruit more collaborators.El Procesamiento del Lenguaje Natural (PLN) es un campo de investigación interdisciplinar de las Ciencias de la Computación, Lingüística y Reconocimiento de Patrones que estudia, entre otros, el uso del lenguaje natural humano en la interacción Hombre-Máquina. La mayoría de las tareas de investigación del PLN se pueden aplicar para resolver problemas del mundo real. Este es el caso del reconocimiento y la traducción del lenguaje natural, que se pueden utilizar para construir sistemas automáticos para la transcripción y traducción de documentos. En cuanto a los documentos manuscritos digitalizados, la transcripción se utiliza para facilitar el acceso digital a los contenidos, ya que la simple digitalización de imágenes sólo proporciona, en la mayoría de los casos, la búsqueda por imagen y no por contenidos lingüísticos. La transcripción es aún más importante en el caso de los manuscritos históricos, ya que la mayoría de estos documentos son únicos y la preservación de su contenido es crucial por razones culturales e históricas. La transcripción de manuscritos históricos suele ser realizada por paleógrafos, que son personas expertas en escritura y vocabulario antiguos. Recientemente, los sistemas de Reconocimiento de Escritura (RES) se han convertido en una herramienta común para ayudar a los paleógrafos en su tarea, la cual proporciona un borrador de la transcripción que los paleógrafos pueden corregir con métodos más o menos sofisticados. Este borrador de transcripción es útil cuando presenta una tasa de error suficientemente reducida para que el proceso de corrección sea más cómodo que una completa transcripción desde cero. Por lo tanto, la obtención de un borrador de transcripción con una baja tasa de error es crucial para que esta tecnología de PLN sea incorporada en el proceso de transcripción. El trabajo descrito en esta tesis se centra en la mejora del borrador de transcripción ofrecido por un sistema RES, con el objetivo de reducir el esfuerzo realizado por los paleógrafos para obtener la transcripción de manuscritos históricos digitalizados. Este problema se enfrenta a partir de tres escenarios diferentes, pero complementarios: · Multimodalidad: El uso de sistemas RES permite a los paleógrafos acelerar el proceso de transcripción manual, ya que son capaces de corregir en un borrador de la transcripción. Otra alternativa es obtener el borrador de la transcripción dictando el contenido a un sistema de Reconocimiento Automático de Habla. Cuando ambas fuentes están disponibles, una combinación multimodal de las mismas es posible y se puede realizar un proceso iterativo para refinar la hipótesis final. · Interactividad: El uso de tecnologías asistenciales en el proceso de transcripción permite reducir el tiempo y el esfuerzo humano requeridos para obtener la transcripción correcta, gracias a la cooperación entre el sistema asistencial y el paleógrafo para obtener la transcripción perfecta. La realimentación multimodal se puede utilizar en el sistema asistencial para proporcionar otras fuentes de información adicionales con señales que representen la misma secuencia de palabras a transcribir (por ejemplo, una imagen de texto, o la señal de habla del dictado del contenido de dicha imagen de texto), o señales que representen sólo una palabra o carácter a corregir (por ejemplo, una palabra manuscrita mediante una pantalla táctil). · Crowdsourcing: La colaboración distribuida y abierta surge como una poderosa herramienta para la transcripción masiva a un costo relativamente bajo, ya que el esfuerzo de supervisión de los paleógrafos puede ser drásticamente reducido. La combinación multimodal permite utilizar el dictado del contenido de líneas de texto manuscrito en una plataforma de crowdsourcing multimodal, donde los colaboradores pueden proporcionar las muestras de habla utilizando su propio dispositivo móvil en lugar de usar ordenadores,El Processament del Llenguatge Natural (PLN) és un camp de recerca interdisciplinar de les Ciències de la Computació, la Lingüística i el Reconeixement de Patrons que estudia, entre d'altres, l'ús del llenguatge natural humà en la interacció Home-Màquina. La majoria de les tasques de recerca del PLN es poden aplicar per resoldre problemes del món real. Aquest és el cas del reconeixement i la traducció del llenguatge natural, que es poden utilitzar per construir sistemes automàtics per a la transcripció i traducció de documents. Quant als documents manuscrits digitalitzats, la transcripció s'utilitza per facilitar l'accés digital als continguts, ja que la simple digitalització d'imatges només proporciona, en la majoria dels casos, la cerca per imatge i no per continguts lingüístics (paraules clau, expressions, categories sintàctiques o semàntiques). La transcripció és encara més important en el cas dels manuscrits històrics, ja que la majoria d'aquests documents són únics i la preservació del seu contingut és crucial per raons culturals i històriques. La transcripció de manuscrits històrics sol ser realitzada per paleògrafs, els quals són persones expertes en escriptura i vocabulari antics. Recentment, els sistemes de Reconeixement d'Escriptura (RES) s'han convertit en una eina comuna per ajudar els paleògrafs en la seua tasca, la qual proporciona un esborrany de la transcripció que els paleògrafs poden esmenar amb mètodes més o menys sofisticats. Aquest esborrany de transcripció és útil quan presenta una taxa d'error prou reduïda perquè el procés de correcció siga més còmode que una completa transcripció des de zero. Per tant, l'obtenció d'un esborrany de transcripció amb un baixa taxa d'error és crucial perquè aquesta tecnologia del PLN siga incorporada en el procés de transcripció. El treball descrit en aquesta tesi se centra en la millora de l'esborrany de la transcripció ofert per un sistema RES, amb l'objectiu de reduir l'esforç realitzat pels paleògrafs per obtenir la transcripció de manuscrits històrics digitalitzats. Aquest problema s'enfronta a partir de tres escenaris diferents, però complementaris: · Multimodalitat: L'ús de sistemes RES permet als paleògrafs accelerar el procés de transcripció manual, ja que són capaços de corregir un esborrany de la transcripció. Una altra alternativa és obtenir l'esborrany de la transcripció dictant el contingut a un sistema de Reconeixement Automàtic de la Parla. Quan les dues fonts (imatge i parla) estan disponibles, una combinació multimodal és possible i es pot realitzar un procés iteratiu per refinar la hipòtesi final. · Interactivitat: L'ús de tecnologies assistencials en el procés de transcripció permet reduir el temps i l'esforç humà requerits per obtenir la transcripció real, gràcies a la cooperació entre el sistema assistencial i el paleògraf per obtenir la transcripció perfecta. La realimentació multimodal es pot utilitzar en el sistema assistencial per proporcionar fonts d'informació addicionals amb senyals que representen la mateixa seqüencia de paraules a transcriure (per exemple, una imatge de text, o el senyal de parla del dictat del contingut d'aquesta imatge de text), o senyals que representen només una paraula o caràcter a corregir (per exemple, una paraula manuscrita mitjançant una pantalla tàctil). · Crowdsourcing: La col·laboració distribuïda i oberta sorgeix com una poderosa eina per a la transcripció massiva a un cost relativament baix, ja que l'esforç de supervisió dels paleògrafs pot ser reduït dràsticament. La combinació multimodal permet utilitzar el dictat del contingut de línies de text manuscrit en una plataforma de crowdsourcing multimodal, on els col·laboradors poden proporcionar les mostres de parla utilitzant el seu propi dispositiu mòbil en lloc d'utilitzar ordinadors d'escriptori o portàtils, la qual cosa permet ampliar el nombrGranell Romero, E. (2017). Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86137TESI

Interactive Transcription of Old Text Documents

Nowadays, there are huge collections of handwritten text documents in libraries all over the world. The high demand for these resources has led to the creation of digital libraries in order to facilitate the preservation and provide electronic access to these documents. However text transcription of these documents im- ages are not always available to allow users to quickly search information, or computers to process the information, search patterns or draw out statistics. The problem is that manual transcription of these documents is an expensive task from both economical and time viewpoints. This thesis presents a novel ap- proach for e cient Computer Assisted Transcription (CAT) of handwritten text documents using state-of-the-art Handwriting Text Recognition (HTR) systems. The objective of CAT approaches is to e ciently complete a transcription task through human-machine collaboration, as the e ort required to generate a manual transcription is high, and automatically generated transcriptions from state-of-the-art systems still do not reach the accuracy required. This thesis is centered on a special application of CAT, that is, the transcription of old text document when the quantity of user e ort available is limited, and thus, the entire document cannot be revised. In this approach, the objective is to generate the best possible transcription by means of the user e ort available. This thesis provides a comprehensive view of the CAT process from feature extraction to user interaction. First, a statistical approach to generalise interactive transcription is pro- posed. As its direct application is unfeasible, some assumptions are made to apply it to two di erent tasks. First, on the interactive transcription of hand- written text documents, and next, on the interactive detection of the document layout. Next, the digitisation and annotation process of two real old text documents is described. This process was carried out because of the scarcity of similar resources and the need of annotated data to thoroughly test all the developed tools and techniques in this thesis. These two documents were carefully selected to represent the general di culties that are encountered when dealing with HTR. Baseline results are presented on these two documents to settle down a benchmark with a standard HTR system. Finally, these annotated documents were made freely available to the community. It must be noted that, all the techniques and methods developed in this thesis have been assessed on these two real old text documents. Then, a CAT approach for HTR when user e ort is limited is studied and extensively tested. The ultimate goal of applying CAT is achieved by putting together three processes. Given a recognised transcription from an HTR system. The rst process consists in locating (possibly) incorrect words and employs the user e ort available to supervise them (if necessary). As most words are not expected to be supervised due to the limited user e ort available, only a few are selected to be revised. The system presents to the user a small subset of these words according to an estimation of their correctness, or to be more precise, according to their con dence level. Next, the second process starts once these low con dence words have been supervised. This process updates the recogni- tion of the document taking user corrections into consideration, which improves the quality of those words that were not revised by the user. Finally, the last process adapts the system from the partially revised (and possibly not perfect) transcription obtained so far. In this adaptation, the system intelligently selects the correct words of the transcription. As results, the adapted system will bet- ter recognise future transcriptions. Transcription experiments using this CAT approach show that this approach is mostly e ective when user e ort is low. The last contribution of this thesis is a method for balancing the nal tran- scription quality and the supervision e ort applied using our previously de- scribed CAT approach. In other words, this method allows the user to control the amount of errors in the transcriptions obtained from a CAT approach. The motivation of this method is to let users decide on the nal quality of the desired documents, as partially erroneous transcriptions can be su cient to convey the meaning, and the user e ort required to transcribe them might be signi cantly lower when compared to obtaining a totally manual transcription. Consequently, the system estimates the minimum user e ort required to reach the amount of error de ned by the user. Error estimation is performed by computing sepa- rately the error produced by each recognised word, and thus, asking the user to only revise the ones in which most errors occur. Additionally, an interactive prototype is presented, which integrates most of the interactive techniques presented in this thesis. This prototype has been developed to be used by palaeographic expert, who do not have any background in HTR technologies. After a slight ne tuning by a HTR expert, the prototype lets the transcribers to manually annotate the document or employ the CAT ap- proach presented. All automatic operations, such as recognition, are performed in background, detaching the transcriber from the details of the system. The prototype was assessed by an expert transcriber and showed to be adequate and e cient for its purpose. The prototype is freely available under a GNU Public Licence (GPL).Serrano Martínez-Santos, N. (2014). Interactive Transcription of Old Text Documents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37979TESI

Building task-oriented machine translation systems

La principal meta de esta tesis es desarrollar sistemas de traduccion interactiva que presenten mayor sinergia con sus usuarios potenciales. Por ello, el objetivo es hacer los sistemas estado del arte mas ergonomicos, intuitivos y eficientes, con el fin de que el experto humano se sienta mas comodo al utilizarlos. Con este fin se presentan diferentes t�ecnicas enfocadas a mejorar la adaptabilidad y el tiempo de respuesta de los sistemas de traduccion automatica subyacentes, as�ÿ como tambien se presenta una estrategia cuya finalidad es mejorar la interaccion hombre-m�aquina. Todo ello con el proposito ultimo de rellenar el hueco existente entre el estado del arte en traduccion automatica y las herramientas que los traductores humanos tienen a su disposici�on. En lo que respecta al tiempo de respuesta de los sistemas de traducci�on autom�atica, en esta tesis se presenta una t�ecnica de poda de los par�ametros de los modelos de traducci�on actuales, cuya intuici�on est�a basada en el concepto de segmentaci�on biling¤ue, pero que termina por evolucionar hacia una estrategia de re-estimaci�on de dichos par�ametros. Utilizando esta estrategia se obtienen resultados experimentales que demuestran que es posible podar la tabla de segmentos hasta en un 97%, sin mermar por ello la calidad de las traducciones obtenidas. Adem�as, estos resultados son coherentes en diferentes pares de lenguas, lo cual evidencia que la t�ecnica que se presenta aqu�ÿ es efectiva en un entorno de traducci�on autom�atica tradicional, y por lo tanto podr�ÿa ser utilizada directamente en un escenario de post-edici�on. Sin embargo, los experimentos llevados a cabo en traducci�on interactiva son ligeramente menos convincentes, pues implican la necesidad de llegar a un compromiso entre el tiempo de respuesta y la calidad de los sufijos producidos. Por otra parte, se presentan dos t�ecnicas de adaptaci�on, con el prop�osito de mejorar la adaptabilidad de los sistemas de traducci�on autom�atica. La primeraSanchis Trilles, G. (2012). Building task-oriented machine translation systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17174Palanci

Anuário Científico – 2011 Resumos de Artigos, Comunicações, Livros e Monografias de Mestrado

Há mais de uma década que o ISEL vem firmando a sua aposta na busca e na divulgação do conhecimento científico na área da Engenharia, assentes na inovação e no desenvolvimento de novas tecnologias, procurando que os resultados alcançados nos projetos de investigação tenham impacto na indústria e na vida dos cidadãos como forma de responder às necessidades cada vez mais complexas e exigentes da sociedade no seu todo. Nesta relação, o ISEL tem contribuído para a evolução da produção e do conhecimento científicos, assumindo, por vezes numa posição de vanguarda, ora em iniciativa própria ora em parceria com diversas instituições, quer de ensino quer do tecido empresarial. Como forma de dar visibilidade ao trabalho desenvolvido pelos docentes (com afiliação ISEL) e alunos do ISEL, o Anuário Científico tornou-se num meio de divulgação privilegiado, estando disponível em acesso livre a toda a comunidade científica mas também a todos os cidadãos, podendo ser consultado em formato eletrónico no sítio institucional do ISEL, bem como no Repositório Científico do Instituto Polítécnico de Lisboa.1 Fazendo uma análise comparativa em relação às publicações referentes a 2009 e a 2010, constata-se que o número de publicações duplicou em 2011