Lenguas de compás acentual y lenguas de compás silábico: revisión teórica e implicaciones pedagógicas

In this paper the traditional rhythmic classification of stress-timed and syllable-timed languages has been revised by means of the analysis of syllable structure, vowel reduction, intersyllabic compression and the strength of the contrast between stressed and unstressed syllables in an English and Spanish global corpus consisting of carrier sentences, read prose and oral samples of spontaneous speech. Obviously, this theoretical revision involves a pedagogical shift from pyramidal exercises devised to achieve isochronous interstress intervals in English to activities focussed on duration differences between stressed and unstressed syllables

Automatic prosodic analysis for computer aided pronunciation teaching

Correct pronunciation of spoken language requires the appropriate modulation of acoustic characteristics of speech to convey linguistic information at a suprasegmental level. Such prosodic modulation is a key aspect of spoken language and is an important component of foreign language learning, for purposes of both comprehension and intelligibility. Computer aided pronunciation teaching involves automatic analysis of the speech of a non-native talker in order to provide a diagnosis of the learner's performance in comparison with the speech of a native talker. This thesis describes research undertaken to automatically analyse the prosodic aspects of speech for computer aided pronunciation teaching. It is necessary to describe the suprasegmental composition of a learner's speech in order to characterise significant deviations from a native-like prosody, and to offer some kind of corrective diagnosis. Phonological theories of prosody aim to describe the suprasegmental composition of speech..

LENGUAS DE COMPÁS ACENTUAL Y LENGUAS DE COMPÁS SILÁBICO. REVISIÓN TEÓRICA E IMPLICACIONES PEDAGÓGICAS

AbstractIn this paper the traditional rhythmic classification of stress-timed and syllable-timed languages has been revised by means of the analysis of syllable structure, vowel reduction, intersyllabic compression and the strength of the contrast between stressed and unstressed syllables in an English and Spanish global corpus consisting of carrier sentences, read prose and oral samples of spontaneous speech. Obviously, this theoretical revision involves a pedagogical shift from pyramidal exercises devised to achieve isochronous interstress intervals in English to activities focussed on duration differences between stressed and unstressed syllables

Predicting and Manipulating the Difficulty of Text-Completion Exercises for Language Learning

The increasing levels of international communication in all aspects of life lead to a growing demand of language skills. Traditional language courses compete nowadays with a wide range of online offerings that promise higher flexibility. However, most platforms provide rather static educational content and do not yet incorporate the recent progress in educational natural language processing. In the last years, many researchers developed new methods for automatic exercise generation, but the generated output is often either too easy or too difficult to be used with real learners. In this thesis, we address the task of predicting and manipulating the difficulty of text-completion exercises based on measurable linguistic properties to bridge the gap between technical ambition and educational needs. The main contribution consists of a theoretical model and a computational implementation for exercise difficulty prediction on the item level. This is the first automatic approach that reaches human performance levels and is applicable to various languages and exercise types. The exercises in this thesis differ with respect to the exercise content and the exercise format. As theoretical basis for the thesis, we develop a new difficulty model that combines content and format factors and further distinguishes the dimensions of text difficulty, word difficulty, candidate ambiguity, and item dependency. It is targeted at text-completion exercises that are a common method for fast language proficiency tests. The empirical basis for the thesis consists of five difficulty datasets containing exercises annotated with learner performance data. The difficulty is expressed as the ratio of learners who fail to solve the exercise. In order to predict the difficulty for unseen exercises, we implement the four dimensions of the model as computational measures. For each dimension, the thesis contains the discussion and implementation of existing measures, the development of new approaches, and an experimental evaluation on sub-tasks. In particular, we developed new approaches for the tasks of cognate production, spelling difficulty prediction, and candidate ambiguity evaluation. For the main experiments, the individual measures are combined into an machine learning approach to predict the difficulty of C-tests, X-tests and cloze tests in English, German, and French. The performance of human experts on the same task is determined by conducting an annotation study to provide a basis for comparison. The quality of the automatic prediction reaches the levels of human accuracy for the largest datasets. If we can predict the difficulty of exercises, we are able to manipulate the difficulty. We develop a new approach for exercise generation and selection that is based on the prediction model. It reaches high acceptance ratings by human users and can be directly integrated into real-world scenarios. In addition, the measures for word difficulty and candidate ambiguity are used to improve the tasks of content and distractor manipulation. Previous work for exercise difficulty was commonly limited to manual correlation analyses using learner results. The computational approach of this thesis makes it possible to predict the difficulty of text-completion exercises in advance. This is an important contribution towards the goal of completely automated exercise generation for language learning

The Lexicon Graph Model : a generic model for multimodal lexicon development

Trippel T. The Lexicon Graph Model : a generic model for multimodal lexicon development. Bielefeld (Germany): Bielefeld University; 2006.Das Lexicon Graph Model stellt ein Modell für Lexika dar, die korpusbasiert sein können und multimodale Informationen enthalten. Hierbei wird die Perspektive der Lexikontheorie eingenommen, wobei die zugrundeliegenden Datenstrukturen sowohl vom Lexikon als auch von Annotationen betrachtet werden. Letztere fallen dadurch in das Blickfeld, weil sie als Grundlage für die Erstellung von Lexika gesehen werden. Der Begriff des Lexikons bezieht sich hier sowohl auf den Bereich des Wörterbuchs als auch der in elektronischen Applikationen integrierten Lexikondatenbanken. Die existierenden Formalismen und Ansätze der Lexikonentwicklung zeigen verschiedene Probleme im Zusammenhang mit Lexika auf, etwa die Zusammenfassung von existierenden Lexika zu einem, die Disambiguierung von Mehrdeutigkeiten im Lexikon auf verschiedenen lexikalischen Ebenen, die Repräsentation von anderen Modalitäten im Lexikon, die Selektion des lexikalischen Schlüsselbegriffs für Lexikonartikel, etc. Der vorliegende Ansatz geht davon aus, dass sich Lexika zwar in ihrem Inhalt, nicht aber in einer grundlegenden Struktur unterscheiden, so dass verschiedenartige Lexika im Rahmen eines Unifikationsprozesses dublettenfrei miteinander verbunden werden können. Hieraus resultieren deklarative Lexika. Für Lexika können diese Graphen mit dem Lexikongraph-Modell wie hier dargestellt modelliert werden. Dabei sind Lexikongraphen analog den von Bird und Libermann beschriebenen Annotationsgraphen gesehen und können daher auch ähnlich verarbeitet werden. Die Untersuchung des Lexikonformalismus beruht auf vier Schritten. Zunächst werden existierende Lexika analysiert und beschrieben. Danach wird mit dem Lexikongraph-Modell eine generische Darstellung von Lexika vorgestellt, die auch implementiert und getestet wird. Basierend auf diesem Formalismus wird die Beziehung zu Annotationsgraphen hergestellt, wobei auch beschrieben wird, welche Maßstäbe an angemessene Annotationen für die Verwendung zur Lexikonentwicklung angelegt werden müssen.The Lexicon Graph Model provides a model and framework for lexicons that can be corpus based and contain multimodal information. The focus is more from the lexicon theory perspective, looking at the underlying data structures that are part of existing lexicons and corpora. The term lexicon in linguistics and artificial intelligence is used in different ways, including traditional print dictionaries in book form, CD-ROM editions, Web based versions of the same, but also computerized resources of similar structures to be used by applications. These applications cover systems for human-machine communication as well as spell checkers. The term lexicon in this work is used as the most generic term covering all lexical applications. Existing formalisms in lexicon development show different problems with lexicons, for example combining different kinds of lexical resources, disambiguation on different lexical levels, the representation of different modalities in a lexicon. The Lexicon Graph Model presupposes that lexicons can have different structures but have fundamentally a similar structure, making it possible to combine lexicons in a unification process, resulting in a declarative lexicon. The underlying model is a graph, the Lexicon Graph, which is modeled similar to Annotation Graphs as described by Bird and Libermann. The investigation of the lexicon formalism contains four steps, that is the analysis of existing lexicons, the introduction of the Lexicon Graph Model as a generic representation for lexicons, the implementation of the formalism in different contexts and an evaluation of the formalism. It is shown that Annotation Graphs and Lexicon Graphs are indeed related not only in their formalism and it is shown, what standards have to be applied to annotations to be usable for lexicon development

Improving Searchability of Automatically Transcribed Lectures Through Dynamic Language Modelling

Recording university lectures through lecture capture systems is increasingly common. However, a single continuous audio recording is often unhelpful for users, who may wish to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set of recordings. A transcript of the recording can enable faster navigation and searching. Automatic speech recognition (ASR) technologies may be used to create automated transcripts, to avoid the significant time and cost involved in manual transcription. Low accuracy of ASR-generated transcripts may however limit their usefulness. In particular, ASR systems optimized for general speech recognition may not recognize the many technical or discipline-specific words occurring in university lectures. To improve the usefulness of ASR transcripts for the purposes of information retrieval (search) and navigating within recordings, the lexicon and language model used by the ASR engine may be dynamically adapted for the topic of each lecture. A prototype is presented which uses the English Wikipedia as a semantically dense, large language corpus to generate a custom lexicon and language model for each lecture from a small set of keywords. Two strategies for extracting a topic-specific subset of Wikipedia articles are investigated: a naïve crawler which follows all article links from a set of seed articles produced by a Wikipedia search from the initial keywords, and a refinement which follows only links to articles sufficiently similar to the parent article. Pair-wise article similarity is computed from a pre-computed vector space model of Wikipedia article term scores generated using latent semantic indexing. The CMU Sphinx4 ASR engine is used to generate transcripts from thirteen recorded lectures from Open Yale Courses, using the English HUB4 language model as a reference and the two topic-specific language models generated for each lecture from Wikipedia

Automatic inference of causal reasoning chains from student essays

While there has been an increasing focus on higher-level thinking skills arising from the Common Core Standards, many high-school and middle-school students struggle to combine and integrate information from multiple sources when writing essays. Writing is an important learning skill, and there is increasing evidence that writing about a topic develops a deeper understanding in the student. However, grading essays is time consuming for teachers, resulting in an increasing focus on shallower forms of assessment that are easier to automate, such as multiple-choice tests. Existing essay grading software has attempted to ease this burden but relies on shallow lexico-syntactic features and is unable to understand the structure or validity of a student’s arguments or explanations. Without the ability to understand a student’s reasoning processes, it is impossible to write automated formative assessment systems to assist students with improving their thinking skills through essay writing. In order to understand the arguments put forth in an explanatory essay in the science domain, we need a method of representing the causal structure of a piece of explanatory text. Psychologists use a representation called a causal model to represent a student\u27s understanding of an explanatory text. This consists of a number of core concepts, and a set of causal relations linking them into one or more causal chains, forming a causal model. In this thesis I present a novel system for automatically constructing causal models from student scientific essays using Natural Language Processing (NLP) techniques. The problem was decomposed into 4 sub-problems - assigning essay concepts to words, detecting causal-relations between these concepts, resolving coreferences within each essay, and using the structure of the whole essay to reconstruct a causal model. Solutions to each of these sub-problems build upon the predictions from the solutions to earlier problems, forming a sequential pipeline of models. Designing a system in this way allows later models to correct for false positive predictions from downstream models. However, this also has the disadvantage that errors made in earlier models can propagate through the system, negatively impacting the upstream models, and limiting their accuracy. Producing robust solutions for the initial 2 sub problems, detecting concepts, and parsing causal relations between them, was critical in building a robust system. A number of sequence labeling models were trained to classify the concepts associated with each word, with the most effective approach being a bidirectional recurrent neural network (RNN), a deep learning model commonly applied to word labeling problems. This is because the RNN used pre-trained word embeddings to better generalize to rarer words, and was able to use information from both ends of each sentence to infer a word\u27s concept. The concepts predicted by this model were then used to develop causal relation parsing models for detecting causal connections between these concepts. A shift-reduce dependency parsing model was trained using the SEARN algorithm and out-performed a number of other approaches by better utilizing the structure of the problem and directly optimizing the error metric used. Two pre-trained coreference resolution systems were used to resolve coreferences within the essays. However a word tagging model trained to predict anaphors combined with a heuristic for determining the antecedent out-performed these two systems. Finally, a model was developed for parsing a causal model from an entire essay, utilizing the solutions to the three previous problems. A beam search algorithm was used to produce multiple parses for each sentence, which in turn were combined to generate multiple candidate causal models for each student essay. A reranking algorithm was then used to select the optimal causal model from all of the generated candidates. An important contribution of this work is that it represents a system for parsing a complete causal model of a scientific essay from a student\u27s written answer. Existing systems have been developed to parse individual causal relations, but no existing system attempts to parse a sequence of linked causal relations forming a causal model from an explanatory scientific essay. It is hoped that this work can lead to the development of more robust essay grading software and formative assessment tools, and can be extended to build solutions for extracting causality from text in other domains. In addition, I also present 2 novel approaches for optimizing the micro-F1 score within the design of two of the algorithms studied: the dependency parser and the reranking algorithm. The dependency parser uses a custom cost function to estimate the impact of parsing mistakes on the overall micro-F1 score, while the reranking algorithm allows the micro-F1 score to be optimized by tuning the beam search parameter to balance recall and precision