14 resultados para Machine Translation
em Universidad Politécnica de Madrid
Resumo:
This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Driver's License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82.
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Este artículo describe una estrategia de selección de frases para hacer el ajuste de un sistema de traducción estadístico basado en el decodificador Moses que traduce del español al inglés. En este trabajo proponemos dos posibilidades para realizar esta selección de las frases del corpus de validación que más se parecen a las frases que queremos traducir (frases de test en lengua origen). Con esta selección podemos obtener unos mejores pesos de los modelos para emplearlos después en el proceso de traducción y, por tanto, mejorar los resultados. Concretamente, con el método de selección basado en la medida de similitud propuesta en este artículo, mejoramos la medida BLEU del 27,17% con el corpus de validación completo al 27,27% seleccionando las frases para el ajuste. Estos resultados se acercan a los del experimento ORACLE: se utilizan las mismas frases de test para hacer el ajuste de los pesos. En este caso, el BLEU obtenido es de 27,51%.
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This paper proposes an architecture, based on statistical machine translation, for developing the text normalization module of a text to speech conversion system. The main target is to generate a language independent text normalization module, based on data and flexible enough to deal with all situa-tions presented in this task. The proposed architecture is composed by three main modules: a tokenizer module for splitting the text input into a token graph (tokenization), a phrase-based translation module (token translation) and a post-processing module for removing some tokens. This paper presents initial exper-iments for numbers and abbreviations. The very good results obtained validate the proposed architecture.
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This paper describes the UPM system for translation task at the EMNLP 2011 workshop on statistical machine translation (http://www.statmt.org/wmt11/), and it has been used for both directions: Spanish-English and English-Spanish. This system is based on Moses with two new modules for pre and post processing the sentences. The main contribution is the method proposed (based on the similarity with the source language test set) for selecting the sentences for training the models and adjusting the weights. With system, we have obtained a 23.2 BLEU for Spanish-English and 21.7 BLEU for EnglishSpanish
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Ontologies and taxonomies are widely used to organize concepts providing the basis for activities such as indexing, and as background knowledge for NLP tasks. As such, translation of these resources would prove useful to adapt these systems to new languages. However, we show that the nature of these resources is significantly different from the "free-text" paradigm used to train most statistical machine translation systems. In particular, we see significant differences in the linguistic nature of these resources and such resources have rich additional semantics. We demonstrate that as a result of these linguistic differences, standard SMT methods, in particular evaluation metrics, can produce poor performance. We then look to the task of leveraging these semantics for translation, which we approach in three ways: by adapting the translation system to the domain of the resource; by examining if semantics can help to predict the syntactic structure used in translation; and by evaluating if we can use existing translated taxonomies to disambiguate translations. We present some early results from these experiments, which shed light on the degree of success we may have with each approach
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Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge.
Resumo:
This paper describes the design, development and field evaluation of a machine translation system from Spanish to Spanish Sign Language (LSE: Lengua de Signos Española). The developed system focuses on helping Deaf people when they want to renew their Driver’s License. The system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs). For the natural language translator, three technological approaches have been implemented and evaluated: an example-based strategy, a rule-based translation method and a statistical translator. For the final version, the implemented language translator combines all the alternatives into a hierarchical structure. This paper includes a detailed description of the field evaluation. This evaluation was carried out in the Local Traffic Office in Toledo involving real government employees and Deaf people. The evaluation includes objective measurements from the system and subjective information from questionnaires. The paper details the main problems found and a discussion on how to solve them (some of them specific for LSE).
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Review of this book, that is the author's Thesis Dissertation.
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OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web
1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS
Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs.
These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools.
Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ‘intelligently’ will include at least a module for POS tagging. The more an application needs to ‘understand’ the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate.
However, linguistic annotation tools have still some limitations, which can be summarised as follows:
1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.).
2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts.
3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc.
A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved.
In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool.
Therefore, it would be quite useful to find a way to
(i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools;
(ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate.
Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned.
Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section.
2. GOALS OF THE PRESENT WORK
As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based
Resumo:
This paper describes the UPM system for the Spanish-English translation task at the NAACL 2012 workshop on statistical machine translation. This system is based on Moses. We have used all available free corpora, cleaning and deleting some repetitions. In this paper, we also propose a technique for selecting the sentences for tuning the system. This technique is based on the similarity with the sentences to translate. With our approach, we improve the BLEU score from 28.37% to 28.57%. And as a result of the WMT12 challenge we have obtained a 31.80% BLEU with the 2012 test set. Finally, we explain different experiments that we have carried out after the competition.
Resumo:
This paper describes the text normalization module of a text to speech fully-trainable conversion system and its application to number transcription. The main target is to generate a language independent text normalization module, based on data instead of on expert rules. This paper proposes a general architecture based on statistical machine translation techniques. This proposal is composed of three main modules: a tokenizer for splitting the text input into a token graph, a phrase-based translation module for token translation, and a post-processing module for removing some tokens. This architecture has been evaluated for number transcription in several languages: English, Spanish and Romanian. Number transcription is an important aspect in the text normalization problem.
Resumo:
El trabajo que se presenta a continuación desarrolla un modelo para calcular la distancia semántica entre dos oraciones representadas por grafos UNL. Este problema se plantea en el contexto de la traducción automática donde diferentes traductores pueden generar oraciones ligeramente diferentes partiendo del mismo original. La medida de distancia que se propone tiene como objetivo proporcionar una evaluación objetiva sobre la calidad del proceso de generación del texto. El autor realiza una exploración del estado del arte sobre esta materia, reuniendo en un único trabajo los modelos propuestos de distancia semántica entre conceptos, los modelos de comparación de grafos y las pocas propuestas realizadas para calcular distancias entre grafos conceptuales. También evalúa los pocos recursos disponibles para poder experimentar el modelo y plantea una metodología para generar los conjuntos de datos que permitirían aplicar la propuesta con el rigor científico necesario y desarrollar la experimentación. Utilizando las piezas anteriores se propone un modelo novedoso de comparación entre grafos conceptuales que permite utilizar diferentes algoritmos de distancia entre conceptos y establecer umbrales de tolerancia para permitir una comparación flexible entre las oraciones. Este modelo se programa utilizando C++, se alimenta con los recursos a los que se ha hecho referencia anteriormente, y se experimenta con un conjunto de oraciones creado por el autor ante la falta de otros recursos disponibles. Los resultados del modelo muestran que la metodología y la implementación pueden conducir a la obtención de una medida de distancia entre grafos UNL con aplicación en sistemas de traducción automática, sin embargo, la carencia de recursos y de datos etiquetados con los que validar el algoritmo requieren un esfuerzo previo importante antes de poder ofrecer resultados concluyentes.---ABSTRACT---The work presented here develops a model to calculate the semantic distance between two sentences represented by their UNL graphs. This problem arises in the context of machine translation where different translators can generate slightly different sentences from the same original. The distance measure that is proposed aims to provide an objective evaluation on the quality of the process involved in the generation of text. The author carries out an exploration of the state of the art on this subject, bringing together in a single work the proposed models of semantic distance between concepts, models for comparison of graphs and the few proposals made to calculate distances between conceptual graphs. It also assesses the few resources available to experience the model and presents a methodology to generate the datasets that would be needed to develop the proposal with the scientific rigor required and to carry out the experimentation. Using the previous parts a new model is proposed to compute differences between conceptual graphs; this model allows the use of different algorithms of distance between concepts and is parametrized in order to be able to perform a flexible comparison between the resulting sentences. This model is implemented in C++ programming language, it is powered with the resources referenced above and is experienced with a set of sentences created by the author due to the lack of other available resources. The results of the model show that the methodology and the implementation can lead to the achievement of a measure of distance between UNL graphs with application in machine translation systems, however, lack of resources and of labeled data to validate the algorithm requires an important effort to be done first in order to be able to provide conclusive results.
Resumo:
La tesis que se presenta tiene como propósito la construcción automática de ontologías a partir de textos, enmarcándose en el área denominada Ontology Learning. Esta disciplina tiene como objetivo automatizar la elaboración de modelos de dominio a partir de fuentes información estructurada o no estructurada, y tuvo su origen con el comienzo del milenio, a raíz del crecimiento exponencial del volumen de información accesible en Internet. Debido a que la mayoría de información se presenta en la web en forma de texto, el aprendizaje automático de ontologías se ha centrado en el análisis de este tipo de fuente, nutriéndose a lo largo de los años de técnicas muy diversas provenientes de áreas como la Recuperación de Información, Extracción de Información, Sumarización y, en general, de áreas relacionadas con el procesamiento del lenguaje natural. La principal contribución de esta tesis consiste en que, a diferencia de la mayoría de las técnicas actuales, el método que se propone no analiza la estructura sintáctica superficial del lenguaje, sino que estudia su nivel semántico profundo. Su objetivo, por tanto, es tratar de deducir el modelo del dominio a partir de la forma con la que se articulan los significados de las oraciones en lenguaje natural. Debido a que el nivel semántico profundo es independiente de la lengua, el método permitirá operar en escenarios multilingües, en los que es necesario combinar información proveniente de textos en diferentes idiomas. Para acceder a este nivel del lenguaje, el método utiliza el modelo de las interlinguas. Estos formalismos, provenientes del área de la traducción automática, permiten representar el significado de las oraciones de forma independiente de la lengua. Se utilizará en concreto UNL (Universal Networking Language), considerado como la única interlingua de propósito general que está normalizada. La aproximación utilizada en esta tesis supone la continuación de trabajos previos realizados tanto por su autor como por el equipo de investigación del que forma parte, en los que se estudió cómo utilizar el modelo de las interlinguas en las áreas de extracción y recuperación de información multilingüe. Básicamente, el procedimiento definido en el método trata de identificar, en la representación UNL de los textos, ciertas regularidades que permiten deducir las piezas de la ontología del dominio. Debido a que UNL es un formalismo basado en redes semánticas, estas regularidades se presentan en forma de grafos, generalizándose en estructuras denominadas patrones lingüísticos. Por otra parte, UNL aún conserva ciertos mecanismos de cohesión del discurso procedentes de los lenguajes naturales, como el fenómeno de la anáfora. Con el fin de aumentar la efectividad en la comprensión de las expresiones, el método provee, como otra contribución relevante, la definición de un algoritmo para la resolución de la anáfora pronominal circunscrita al modelo de la interlingua, limitada al caso de pronombres personales de tercera persona cuando su antecedente es un nombre propio. El método propuesto se sustenta en la definición de un marco formal, que ha debido elaborarse adaptando ciertas definiciones provenientes de la teoría de grafos e incorporando otras nuevas, con el objetivo de ubicar las nociones de expresión UNL, patrón lingüístico y las operaciones de encaje de patrones, que son la base de los procesos del método. Tanto el marco formal como todos los procesos que define el método se han implementado con el fin de realizar la experimentación, aplicándose sobre un artículo de la colección EOLSS “Encyclopedia of Life Support Systems” de la UNESCO. ABSTRACT The purpose of this thesis is the automatic construction of ontologies from texts. This thesis is set within the area of Ontology Learning. This discipline aims to automatize domain models from structured or unstructured information sources, and had its origin with the beginning of the millennium, as a result of the exponential growth in the volume of information accessible on the Internet. Since most information is presented on the web in the form of text, the automatic ontology learning is focused on the analysis of this type of source, nourished over the years by very different techniques from areas such as Information Retrieval, Information Extraction, Summarization and, in general, by areas related to natural language processing. The main contribution of this thesis consists of, in contrast with the majority of current techniques, the fact that the method proposed does not analyze the syntactic surface structure of the language, but explores his deep semantic level. Its objective, therefore, is trying to infer the domain model from the way the meanings of the sentences are articulated in natural language. Since the deep semantic level does not depend on the language, the method will allow to operate in multilingual scenarios, where it is necessary to combine information from texts in different languages. To access to this level of the language, the method uses the interlingua model. These formalisms, coming from the area of machine translation, allow to represent the meaning of the sentences independently of the language. In this particular case, UNL (Universal Networking Language) will be used, which considered to be the only interlingua of general purpose that is standardized. The approach used in this thesis corresponds to the continuation of previous works carried out both by the author of this thesis and by the research group of which he is part, in which it is studied how to use the interlingua model in the areas of multilingual information extraction and retrieval. Basically, the procedure defined in the method tries to identify certain regularities at the UNL representation of texts that allow the deduction of the parts of the ontology of the domain. Since UNL is a formalism based on semantic networks, these regularities are presented in the form of graphs, generalizing in structures called linguistic patterns. On the other hand, UNL still preserves certain mechanisms of discourse cohesion from natural languages, such as the phenomenon of the anaphora. In order to increase the effectiveness in the understanding of expressions, the method provides, as another significant contribution, the definition of an algorithm for the resolution of pronominal anaphora limited to the model of the interlingua, in the case of third person personal pronouns when its antecedent is a proper noun. The proposed method is based on the definition of a formal framework, adapting some definitions from Graph Theory and incorporating new ones, in order to locate the notions of UNL expression and linguistic pattern, as well as the operations of pattern matching, which are the basis of the method processes. Both the formal framework and all the processes that define the method have been implemented in order to carry out the experimentation, applying on an article of the "Encyclopedia of Life Support Systems" of the UNESCO-EOLSS collection.
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BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy.