Marco para parsing predictivo interactivo aplicado a la lengua castellana

El marco teórico de Parsing Predictivo Interactivo (IPP) permite construir sistemas de anotación sintáctica interactivos. Los anotadores humanos pueden utilizar estos sistemas de ayuda para crear árboles sintácticos con muy poco esfuerzo (en comparación con el trabajo requerido para corregir manualmente árboles obtenidos a partir de un analizador sintáctico completamente automático). En este artículo se presenta la adaptación a la lengua castellana del marco IPP y su herramienta de anotación IPP-Ann, usando modelos obtenidos a partir del UAM Spanish Treebank. Hemos llevado a cabo experimentación simulando al usuario para obtener métricas de evaluación objetivas para nuestro sistema. Estos resultados muestran que el marco IPP aplicado al UAM Spanish Treebank se traduce en una importante cantidad de esfuerzo ahorrado, comparable con el obtenido al aplicar el marco IPP para analizar la lengua inglesa mediante el Penn Treebank.The Interactive Predictive Parsing (IPP) framework allows us the construction of interactive tree annotation systems. These can help human annotators in creating error-free parse trees with little effort (compared to manually post-editing the trees obtained from a completely automatic parser). In this paper we adapt the IPP framework and the IPP-Ann annotation tool for parse of the Spanish language, by using models obtained from the UAM Spanish Treebank. We performed user simulation experimentation and obtained objective evaluation metrics. The results establish that the IPP framework over the UAM Treebank shows important amounts of user effort reduction, comparable to the gains obtained when applying IPP to the English language on the Penn Treebank.Work supported by the EC (FEDER, FSE), the Spanish Government and Generalitat Valenciana (MICINN, ”Plan E”, under grants MIPRCV ”Consolider Ingenio 2010” CSD2007-00018, MIT-TRAL TIN2009-14633-C03-01, ALMPR Prometeo/2009/014 and FPU AP2006-01363)

Multimodal Interactive Parsing

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38628-2_57Probabilistic parsing is a fundamental problem in Computational Linguistics, whose goal is obtaining a syntactic structure associated to a sentence according to a probabilistic grammatical model. Recently, an interactive framework for probabilistic parsing has been introduced, in which the user and the system cooperate to generate error-free parse trees. In an early prototype developed according to this interactive parsing technology, user feedback was provided by means of mouse actions and keyboard strokes. Here we augment the interaction style with support for (non-deterministic) natural handwritten recognition, and provide confidence measures as a visual aid to ease the correction process. Handwriting input seems to be a modality specially suitable for parsing, since the vocabulary size involved in the recognition of syntactic labels is fairly limited and thus intuitively errors should be small. However, errors may increase as handwriting quality (i.e., calligraphy) degrades. To solve this problem, we introduce a late fusion approach that leverages both on-line and off-line information, corresponding to pen strokes and contextual information from the parse trees. We demonstrate that late fusion can effectively help to disambiguate user intention and improve system accuracy.This research has received funding from the EC’s 7th Framework Programme (FP7/2007-13) under grant agreement No.287576- CasMaCat; from the Spanish MEC under the STraDA project (TIN2012-37475- C02-01) and the MITTRAL project (TIN2009-14633-C03-01); from the GV under the Prometeo project; and from the Universidad del Cauca (Colombia)Benedí Ruiz, JM.; Sánchez Peiró, JA.; Leiva, LA.; Sánchez Sáez, R.; Maca, M. (2013). Multimodal Interactive Parsing. En Pattern Recognition and Image Analysis. Springer. 484-491. https://doi.org/10.1007/978-3-642-38628-2_57S484491Afonso, S., Bick, E., Haber, R., Santos, D.: Floresta sintá(c)tica: a treebank for portuguese. In: Proc. LREC, pp. 1698–1703 (2002)Brants, T., Plaehn, O.: Interactive corpus annotation. In: Proc. LREC (2000)Guyon, I., Schomaker, L., Plamondon, R., Liberman, M., Janet, S.: UNIPEN project of on-line data exchange and recognizer benchmarks. In: Proc. ICPR, pp. 29–33 (1994)Lease, M., Charniak, E., Johnson, M., McClosky, D.: A look at parsing and its applications. In: Proc. AAAI, pp. 1642–1645 (2006)Marcus, M.P., Santorini, B., Marcinkiewicz, M.A.: Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics 19(2), 313–330 (1993)Ortiz, D., Leiva, L.A., Alabau, V., Casacuberta, F.: Interactive machine translation using a web-based architecture. In: Proc. IUI, pp. 423–425 (2010)Romero, V., Leiva, L.A., Toselli, A.H., Vidal, E.: Interactive multimodal transcription of text images using a web-based demo system. In: Proc. IUI, pp. 477–478 (2009)Sánchez-Sáez, R., Leiva, L.A., Sánchez, J.A., Benedí, J.M.: Interactive predictive parsing using a web-based architecture. In: Proc. NAACL-HLT, pp. 37–40 (2010)Sánchez-Sáez, R., Sánchez, J.A., Benedí, J.M.: Interactive predictive parsing. In: Proc. IWPT, pp. 222–225 (2009)Sánchez-Sáez, R., Sánchez, J.A., Benedí, J.M.: Confidence measures for error discrimination in an interactive predictive parsing framework. In: Proc. COLING, pp. 1220–1228 (2010

Discriminative estimation of probabilistic context-free grammars for mathematical expression recognition and retrieval

[EN] We present a discriminative learning algorithm for the probabilistic estimation of two-dimensional probabilistic context-free grammars (2D-PCFG) for mathematical expressions recognition and retrieval. This algorithm is based on a generalization of the H-criterion as the objective function and the growth transformations as the optimization method. For the development of the discriminative estimation algorithm, the N-best interpretations provided by the 2D-PCFG have been considered. Experimental results are reported on two available datasets: Im2Latex and IBEM. The first experiment compares the proposed discriminative estimation method with the classic Viterbi-based estimation method. The second one studies the performance of the estimated models depending on the length of the mathematical expressions and the number of admissible errors in the metric used.This research has been developed with the support of Grant PID2020-116813RBI00a funded by MCIN/AEI/ 10.13039/501100011033 and FPI grant CIACIF/2021/313 funded by Generalitat Valenciana. Universitat Politecnica de Valencia Grant No. SP20210263Noya García, E.; Benedí Ruiz, JM.; Sánchez Peiró, JA.; Anitei, D. (2023). Discriminative estimation of probabilistic context-free grammars for mathematical expression recognition and retrieval. Pattern Analysis and Applications. 26:1571-1584. https://doi.org/10.1007/s10044-023-01158-81571158426Bahl LR, Jelinek F, Mercer RL (1983) A maximum likelihood approach to continuous speech recognition. IEEE Trans Pattern Anal Machine Intell 5(2):179–190Koehn P (2009) Statistical Machine Translation. 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Comput Speech Lang 11(1):43–72. https://doi.org/10.1006/csla.1996.0022Noya E, Sánchez JA, Benedí JM (2021) Generation of Hypergraphs from the N-Best Parsing of 2D-Probabilistic Context-Free Grammars for Mathematical Expression Recognition. In: ICPR, pp 5696–5703. https://doi.org/10.1109/ICPR48806.2021.9412273Ueffing N, Och FJ, Ney H (2002) Generation of word graphs in statistical machine translation. In: Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP 2002), pp 156–163. Association for Computational Linguistics, ???. https://doi.org/10.3115/1118693.1118714. https://aclanthology.org/W02-1021Toselli AH, Vidal E, Puigcerver J, Noya-García E (2019) Probabilistic multi-word spotting in handwritten text images. Pattern Anal Appl 22:23–32. https://doi.org/10.1007/s10044-018-0742-zSánchez-Sáez R, Sánchez JA, Benedí JM (2010) Confidence measures for error discrimination in an interactive predictive parsing framework. 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In: ICDAR, pp 783–795. https://doi.org/10.1007/978-3-030-86337-1_52Gopalakrishnan PS, Kanevsky D, Nadas A, Nahamoo D (1991) An inequality for rational functions with applications to some statistical estimation problems. IEEE Trans Inf Theory 37(1):107–113. https://doi.org/10.1109/18.61108Maca M, Benedí JM, Sánchez JA (2021) Discriminative Learning for Probabilistic Context-Free Grammars based on Generalized H-Criterion. Preprint arXiv:2103.08656arXiv:2103.08656 [cs.CL]Woodland PC, Povey D (2002) Large scale discriminative training of hidden Markov models for speech recognition. Comput Speech Lang 16(1):25–47. https://doi.org/10.1006/csla.2001.0182Noya E, Benedí JM, Sánchez JA, Anitei D (2022) Discriminative learning of two-dimensional probabilistic context-free grammars for mathematical expression recognition and retrieval. In: IbPRIA, pp 333–347. https://doi.org/10.1007/978-3-031-04881-4_27Zanibbi R, Blostein D (2011) Recognition and Retrieval of Mathematical Expressions. IJDAR 15:331–357. https://doi.org/10.1007/s10032-011-0174-4Huang J, Tan J, Bi N (2020) Overview of mathematical expression recognition. In: Pattern recognition and artificial intelligence, pp 41–54. https://doi.org/10.1007/978-3-030-59830-3_4Mahdavi M, Zanibbi R, Mouchere H, Viard-Gaudin C, Garain U (2019) ICDAR 2019 CROHME + TFD: Competition on recognition of handwritten mathematical expressions and typeset formula detection. In: ICDAR, pp 1533–1538. https://doi.org/10.1109/ICDAR.2019.00247Wang DH, Yin F, Wu JW, Yan YP, Huang ZC, Chen GY, Wang Y, Liu CL (2020) ICFHR 2020 Competition on offline recognition and spotting of handwritten mathematical expressions - OffRaSHME. In: ICFHR, pp. 211–215. https://doi.org/10.1109/ICFHR2020.2020.00047Wan Z, Fan K, Wang Q, Zhang S (2019) Recognition of printed mathematical formula symbols based on convolutional neural network. 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Diverse Contributions to Implicit Human-Computer Interaction

Cuando las personas interactúan con los ordenadores, hay mucha información que no se proporciona a propósito. Mediante el estudio de estas interacciones implícitas es posible entender qué características de la interfaz de usuario son beneficiosas (o no), derivando así en implicaciones para el diseño de futuros sistemas interactivos. La principal ventaja de aprovechar datos implícitos del usuario en aplicaciones informáticas es que cualquier interacción con el sistema puede contribuir a mejorar su utilidad. Además, dichos datos eliminan el coste de tener que interrumpir al usuario para que envíe información explícitamente sobre un tema que en principio no tiene por qué guardar relación con la intención de utilizar el sistema. Por el contrario, en ocasiones las interacciones implícitas no proporcionan datos claros y concretos. Por ello, hay que prestar especial atención a la manera de gestionar esta fuente de información. El propósito de esta investigación es doble: 1) aplicar una nueva visión tanto al diseño como al desarrollo de aplicaciones que puedan reaccionar consecuentemente a las interacciones implícitas del usuario, y 2) proporcionar una serie de metodologías para la evaluación de dichos sistemas interactivos. Cinco escenarios sirven para ilustrar la viabilidad y la adecuación del marco de trabajo de la tesis. Resultados empíricos con usuarios reales demuestran que aprovechar la interacción implícita es un medio tanto adecuado como conveniente para mejorar de múltiples maneras los sistemas interactivos.Leiva Torres, LA. (2012). Diverse Contributions to Implicit Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/17803Palanci

Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. 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Advances in Interactive Speech Transcription

[ES] Novedoso sistema interactivo para la transcripción del habla que compensa el esfuerzo del usuario y el error máximo tolerado en las transcripciones resultantes.[EN] Novel interactive speech transcription system that balances the user effort and the maximum allowed error tolerated for the final resulting transcriptions.Sánchez Cortina, I. (2012). Advances in Interactive Speech Transcription. http://hdl.handle.net/10251/17889Archivo delegad