Le chunking perceptif de la parole : sur la nature du groupement temporel et son effet sur la mémoire immédiate

Dans de nombreux comportements qui reposent sur le rappel et la production de séquences, des groupements temporels émergent spontanément, créés par des délais ou des allongements. Ce « chunking » a été observé tant chez les humains que chez certains animaux et plusieurs auteurs l’attribuent à un processus général de chunking perceptif qui est conforme à la capacité de la mémoire à court terme. Cependant, aucune étude n’a établi comment ce chunking perceptif s’applique à la parole. Nous présentons une recension de la littérature qui fait ressortir certains problèmes critiques qui ont nui à la recherche sur cette question. C’est en revoyant ces problèmes qu’on propose une démonstration spécifique du chunking perceptif de la parole et de l’effet de ce processus sur la mémoire immédiate (ou mémoire de travail). Ces deux thèmes de notre thèse sont présentés séparément dans deux articles. Article 1 : The perceptual chunking of speech: a demonstration using ERPs Afin d’observer le chunking de la parole en temps réel, nous avons utilisé un paradigme de potentiels évoqués (PÉ) propice à susciter la Closure Positive Shift (CPS), une composante associée, entre autres, au traitement de marques de groupes prosodiques. Nos stimuli consistaient en des énoncés et des séries de syllabes sans sens comprenant des groupes intonatifs et des marques de groupements temporels qui pouvaient concorder, ou non, avec les marques de groupes intonatifs. Les analyses démontrent que la CPS est suscitée spécifiquement par les allongements marquant la fin des groupes temporels, indépendamment des autres variables. Notons que ces marques d’allongement, qui apparaissent universellement dans la langue parlée, créent le même type de chunking que celui qui émerge lors de l’apprentissage de séquences par des humains et des animaux. Nos résultats appuient donc l’idée que l’auditeur chunk la parole en groupes temporels et que ce chunking perceptif opère de façon similaire avec des comportements verbaux et non verbaux. Par ailleurs, les observations de l’Article 1 remettent en question des études où on associe la CPS au traitement de syntagmes intonatifs sans considérer les effets de marques temporels. Article 2 : Perceptual chunking and its effect on memory in speech processing:ERP and behavioral evidence Nous avons aussi observé comment le chunking perceptif d’énoncés en groupes temporels de différentes tailles influence la mémoire immédiate d’éléments entendus. Afin d’observer ces effets, nous avons utilisé des mesures comportementales et des PÉ, dont la composante N400 qui permettait d’évaluer la qualité de la trace mnésique d’éléments cibles étendus dans des groupes temporels. La modulation de l’amplitude relative de la N400 montre que les cibles présentées dans des groupes de 3 syllabes ont bénéficié d’une meilleure mise en mémoire immédiate que celles présentées dans des groupes plus longs. D’autres mesures comportementales et une analyse de la composante P300 ont aussi permis d’isoler l’effet de la position du groupe temporel (dans l’énoncé) sur les processus de mise en mémoire. Les études ci-dessus sont les premières à démontrer le chunking perceptif de la parole en temps réel et ses effets sur la mémoire immédiate d’éléments entendus. Dans l’ensemble, nos résultats suggèrent qu’un processus général de chunking perceptif favorise la mise en mémoire d’information séquentielle et une interprétation de la parole « chunk par chunk ».In numerous behaviors involving the learning and production of sequences, temporal groups emerge spontaneously, created by delays or a lengthening of elements. This chunking has been observed across behaviors of both humans and animals and is taken to reflect a general process of perceptual chunking that conforms to capacity limits of short-term memory. Yet, no research has determined how perceptual chunking applies to speech. We provide a literature review that bears out critical problems, which have hampered research on this question. Consideration of these problems motivates a principled demonstration that aims to show how perceptual chunking applies to speech and the effect of this process on immediate memory (or “working memory”). These two themes are presented in separate papers in the format of journal articles. Paper 1: The perceptual chunking of speech: a demonstration using ERPs To observe perceptual chunking on line, we use event-related potentials (ERPs) and refer to the neural component of Closure Positive Shift (CPS), which is known to capture listeners’ responses to marks of prosodic groups. The speech stimuli were utterances and sequences of nonsense syllables, which contained intonation phrases marked by pitch, and both phrase-internal and phrase-final temporal groups marked by lengthening. Analyses of CPSs show that, across conditions, listeners specifically perceive speech in terms of chunks marked by lengthening. These lengthening marks, which appear universally in languages, create the same type of chunking as that which emerges in sequence learning by humans and animals. This finding supports the view that listeners chunk speech in temporal groups and that this perceptual chunking operates similarly for speech and non-verbal behaviors. Moreover, the results question reports that relate CPS to intonation phrasing without considering the effects of temporal marks. Paper 2: Perceptual chunking and its effect on memory in speech processing: ERP and behavioral evidence We examined how the perceptual chunking of utterances in terms of temporal groups of differing size influences immediate memory of heard speech. To weigh these effects, we used behavioural measures and ERPs, especially the N400 component, which served to evaluate the quality of the memory trace for target lexemes heard in the temporal groups. Variations in the amplitude of the N400 showed a better memory trace for lexemes presented in groups of 3 syllables compared to those in groups of 4 syllables. Response times along with P300 components revealed effects of position of the chunk in the utterance. This is the first study to demonstrate the perceptual chunking of speech on-line and its effects on immediate memory of heard elements. Taken together the results suggest that a general perceptual chunking enhances a buffering of sequential information and a processing of speech on a chunk-by-chunk basis

Organisation of Japanese prosody

This thesis is an experimental phonological study of pitch in Tokyo Japanese. It comprises five chapters all discussing prosodic processes and phenomena relating to accent, tone or intonation on the basis of experimental evidence. The discussion in each chapter is developed essentially in the following three steps: (i) a critical review or overview of the past work on the subject discussed in the chapter or section; (ii) presentation of new evidence mostly from instrumental experiments; (iii) a discussion of the experimental evidence in theoretical contexts. After outlining the nature and function of word accent in Chapter One, I discuss in Chapter Two the prosodic compound formation process which has traditionally been described as an accent (re)assignment process. I analyze the linguistic structures of those compounds which are not subject to the compound accent rules, and propose several factors which constrain the prosodic compound formation process, defining them as the linguistic conditions on the process. Chapters Three through Five deal with word accent in a wider context of speech, discussing its roles, behavior and phonetic realization in phrase or sentence perspective. Chapter Three discusses the phonetics and phonology of 'accentual fall, ' 'accentual boost' and 'accent clash, ' for each of which the fallacies underlying the impressionistic descriptions in the literature are demonstrated. Four discusses various problems relating to intonational phrases and phrasing. The first part of the chapter focuses on the definition of the two intonational phrases, 'major phrase' and 'minor phrase' while the second part of the chapter explores the linguistic conditions on 'minor phrase formation, ' the intonational phrasing process whereby two or more syntactic/morphological units are combined to form one minor intonational phrase. Chapter Five examines the linguistic structure of 'downtrend, ' the phenomenon whereby pitch declines during the course of utterances. It is shown in the first part of the chapter that Poser's 'catathesis' (downstep) model is a largely adequate model of the intonational phenomenon. After confirming that the trigger of the downtrend phenomenon is largely attributable to accent, it is shown in the second part of the chapter that this accent-triggered process varies considerably depending on the syntactic structure of the phrase or sentence involved, or, in other words, that the configuration of downstep serves to disambiguate otherwise ambiguous syntactic structures. In the course of discussing the specific topics just mentioned, several more general theoretical issues are addressed, including the following four topics: the relation between syntactic structure and phonological structure; the organization of rhythmic structure; the abstractness of phonological (tonal) representation; and the nature of phonetic realization rules

Exploring Language Mechanisms: The Mass-Count Distinction and The Potts Neural Network

The aim of this thesis is to explore language mechanisms in two aspects. First, the statistical properties of syntax and semantics, and second, the neural mechanisms which could be of possible use in trying to understand how the brain learns those particular statistical properties. In the first part of the thesis (part A) we focus our attention on a detailed statistical study of the syntax and semantics of the mass-count distinction in nouns. We collected a database of how 1,434 nouns are used with respect to the mass-count distinction in six languages; additional informants characterised the semantics of the underlying concepts. Results indicate only weak correlations between semantics and syntactic usage. The classification rather than being bimodal, is a graded distribution and it is similar across languages, but syntactic classes do not map onto each other, nor do they reflect, beyond weak correlations, semantic attributes of the concepts. These findings are in line with the hypothesis that much of the mass/count syntax emerges from language- and even speaker-specific grammaticalisation. Further, in chapter 3 we test the ability of a simple neural network to learn the syntactic and semantic relations of nouns, in the hope that it may throw some light on the challenges in modelling the acquisition of the mass-count syntax. It is shown that even though a simple self-organising neural network is insufficient to learn a mapping implementing a syntactic- semantic link, it does however show that the network was able to extract the concept of 'count', and to some extent that of \u2018mass\u2019 as well, without any explicit definition, from both the syntactic and from the semantic data. The second part of the thesis (part B) is dedicated to studying the properties of the Potts neural network. The Potts neural network with its adaptive dynamics represents a simplified model of cortical mechanisms. Among other cognitive phenomena, it intends to model language production by utilising the latching behaviour seen in the network. We expect that a model of language processing should robustly handle various syntactic- semantic correlations amongst the words of a language. With this aim, we test the effect on storage capacity of the Potts network when the memories stored in it share non trivial correlations. Increase in interference between stored memories due to correlations is studied along with modifications in learning rules to reduce the interference. We find that when strongly correlated memories are incorporated in the storage capacity definition, the network is able to regain its storage capacity for low sparsity. Strong correlations also affect the latching behaviour of the Potts network with the network unable to latch from one memory to another. However latching is shown to be restored by modifying the learning rule. Lastly, we look at another feature of the Potts neural network, the indication that it may exhibit spin-glass characteristics. The network is consistently shown to exhibit multiple stable degenerate energy states other than that of pure memories. This is tested for different degrees of correlations in patterns, low and high connectivity, and different levels of global and local noise. We state some of the implications that the spin-glass nature of the Potts neural network may have on language processing

Proceedings of the Conference on Natural Language Processing 2010

This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion

Tesis por compendio[ES] La argumentación computacional es el área de investigación que estudia y analiza el uso de distintas técnicas y algoritmos que aproximan el razonamiento argumentativo humano desde un punto de vista computacional. En esta tesis doctoral se estudia el uso de distintas técnicas propuestas bajo el marco de la argumentación computacional para realizar un análisis automático del discurso argumentativo, y para desarrollar técnicas de persuasión computacional basadas en argumentos. Con estos objetivos, en primer lugar se presenta una completa revisión del estado del arte y se propone una clasificación de los trabajos existentes en el área de la argumentación computacional. Esta revisión nos permite contextualizar y entender la investigación previa de forma más clara desde la perspectiva humana del razonamiento argumentativo, así como identificar las principales limitaciones y futuras tendencias de la investigación realizada en argumentación computacional. En segundo lugar, con el objetivo de solucionar algunas de estas limitaciones, se ha creado y descrito un nuevo conjunto de datos que permite abordar nuevos retos y investigar problemas previamente inabordables (e.g., evaluación automática de debates orales). Conjuntamente con estos datos, se propone un nuevo sistema para la extracción automática de argumentos y se realiza el análisis comparativo de distintas técnicas para esta misma tarea. Además, se propone un nuevo algoritmo para la evaluación automática de debates argumentativos y se prueba con debates humanos reales. Finalmente, en tercer lugar se presentan una serie de estudios y propuestas para mejorar la capacidad persuasiva de sistemas de argumentación computacionales en la interacción con usuarios humanos. De esta forma, en esta tesis se presentan avances en cada una de las partes principales del proceso de argumentación computacional (i.e., extracción automática de argumentos, representación del conocimiento y razonamiento basados en argumentos, e interacción humano-computador basada en argumentos), así como se proponen algunos de los cimientos esenciales para el análisis automático completo de discursos argumentativos en lenguaje natural.[CA] L'argumentació computacional és l'àrea de recerca que estudia i analitza l'ús de distintes tècniques i algoritmes que aproximen el raonament argumentatiu humà des d'un punt de vista computacional. En aquesta tesi doctoral s'estudia l'ús de distintes tècniques proposades sota el marc de l'argumentació computacional per a realitzar una anàlisi automàtic del discurs argumentatiu, i per a desenvolupar tècniques de persuasió computacional basades en arguments. Amb aquestos objectius, en primer lloc es presenta una completa revisió de l'estat de l'art i es proposa una classificació dels treballs existents en l'àrea de l'argumentació computacional. Aquesta revisió permet contextualitzar i entendre la investigació previa de forma més clara des de la perspectiva humana del raonament argumentatiu, així com identificar les principals limitacions i futures tendències de la investigació realitzada en argumentació computacional. En segon lloc, amb l'objectiu de sol$\cdot$lucionar algunes d'aquestes limitacions, hem creat i descrit un nou conjunt de dades que ens permet abordar nous reptes i investigar problemes prèviament inabordables (e.g., avaluació automàtica de debats orals). Conjuntament amb aquestes dades, es proposa un nou sistema per a l'extracció d'arguments i es realitza l'anàlisi comparativa de distintes tècniques per a aquesta mateixa tasca. A més a més, es proposa un nou algoritme per a l'avaluació automàtica de debats argumentatius i es prova amb debats humans reals. Finalment, en tercer lloc es presenten una sèrie d'estudis i propostes per a millorar la capacitat persuasiva de sistemes d'argumentació computacionals en la interacció amb usuaris humans. D'aquesta forma, en aquesta tesi es presenten avanços en cada una de les parts principals del procés d'argumentació computacional (i.e., l'extracció automàtica d'arguments, la representació del coneixement i raonament basats en arguments, i la interacció humà-computador basada en arguments), així com es proposen alguns dels fonaments essencials per a l'anàlisi automàtica completa de discursos argumentatius en llenguatge natural.[EN] Computational argumentation is the area of research that studies and analyses the use of different techniques and algorithms that approximate human argumentative reasoning from a computational viewpoint. In this doctoral thesis we study the use of different techniques proposed under the framework of computational argumentation to perform an automatic analysis of argumentative discourse, and to develop argument-based computational persuasion techniques. With these objectives in mind, we first present a complete review of the state of the art and propose a classification of existing works in the area of computational argumentation. This review allows us to contextualise and understand the previous research more clearly from the human perspective of argumentative reasoning, and to identify the main limitations and future trends of the research done in computational argumentation. Secondly, to overcome some of these limitations, we create and describe a new corpus that allows us to address new challenges and investigate on previously unexplored problems (e.g., automatic evaluation of spoken debates). In conjunction with this data, a new system for argument mining is proposed and a comparative analysis of different techniques for this same task is carried out. In addition, we propose a new algorithm for the automatic evaluation of argumentative debates and we evaluate it with real human debates. Thirdly, a series of studies and proposals are presented to improve the persuasiveness of computational argumentation systems in the interaction with human users. In this way, this thesis presents advances in each of the main parts of the computational argumentation process (i.e., argument mining, argument-based knowledge representation and reasoning, and argument-based human-computer interaction), and proposes some of the essential foundations for the complete automatic analysis of natural language argumentative discourses.This thesis has been partially supported by the Generalitat Valenciana project PROME- TEO/2018/002 and by the Spanish Government projects TIN2017-89156-R and PID2020- 113416RB-I00.Ruiz Dolz, R. (2023). Computational Argumentation for the Automatic Analysis of Argumentative Discourse and Human Persuasion [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/194806Compendi

Phonological and Language Improvements in Preschool Children: A Comparison of Phonological Process Targeting and Whole Language Training.

This study compared phonological and language changes that occurred in preschool phonologically-impaired children following treatment via a discrete phonological process targeting approach or a whole language approach. It was hypothesized that a treatment program utilizing a communication-based, whole language approach would result in improvements in various language domains (e.g., phonology, morphology, syntax, semantics, and pragmatics), while treatment targeting a specific aspect of language, that is, phonology, would result in improvements limited to that specific domain. Subjects were eight preschool children, 3-4 years of age, exhibiting multiple articulation errors, and randomly assigned to one of two intervention programs for a six-week period. The phonological process approach targeted the most salient error pattern exhibited by subjects in this group (i.e., Consonant Cluster Reduction or Fronting) through practice in production and perception of affected minimal pair contrasts. The whole language approach focused on improving the child\u27s ability to formulate and express useful language in a communicative setting through production of narratives, while expanding and increasing complexity of narrative structure. Pretreatment and posttreatment measures of phonological and language performance were used to compare the efficacy of the two treatment approaches. The assessment battery included assessment of single word performance on tests administered, connected speech performance on various tasks (e.g., storytelling; relating familiar experiences) and higher level language performance, including syntactic, semantic, and pragmatic measures. Data analysis revealed that while all subjects demonstrated improved phonological performance, subjects in the whole language group demonstrated a greater degree of improvement than those in the phonological process group. In addition, the whole language group showed larger gains in syntactic, morphological, semantic, and pragmatic expression. These results suggest the need for further studies that evaluate treatment efficacy by utilizing a whole language approach as compared to a discrete phonological approach with young phonologically-impaired children

An investigation into the use of genetic algorithms for shape recognition

The use of the genetic algorithm for shape recognition has been investigated in relation to features along a shape boundary contour. Various methods for encoding chromosomes were investigated, the most successful of which led to the development of a new technique to input normalised 'perceptually important point' features from the contour into a genetic algorithm. Chromosomes evolve with genes defining various ways of 'observing' different parts of the contour. The normalisation process provides the capability for multi-scale spatial frequency filtering and fine/coarse resolution of the contour features. A standard genetic algorithm was chosen for this investigation because its performance can be analysed by applying schema analysis to the genes. A new method for measurement of gene diversity has been developed. It is shown that this diversity measure can be used to direct the genetic algorithm parameters to evolve a number of 'good' chromosomes. In this way a variety of sections along the contour can be observed. A new and effective recognition technique has been developed which makes use of these 'good' chromosomes and the same fitness calculation as used in the genetic algorithm. Correct recognition can be achieved by selecting chromosomes and adjusting two thresholds, the values of which are found not to be critical. Difficulties associated with the calculation of a shape's fitness were analysed and the structure of the genes in the chromosome investigated using schema and epistatic analysis. It was shown that the behaviour of the genetic algorithm is compatible with the schema theorem of J. H. Holland. Reasons are given to explain the minimum value for the mutation probability that is required for the evolution of a number of' good' chromosomes. Suggestions for future research are made and, in particular, it is recommended that the convergence properties of the standard genetic algorithm be investigated

A Lexicalized Tree Adjoining Grammar for English

This document describes a sizable grammar of English written in the TAG formalism and implemented for use with the XTAG system. This report and the grammar described herein supersedes the TAG grammar described in an earlier 1995 XTAG technical report. The English grammar described in this report is based on the TAG formalism which has been extended to include lexicalization, and unification-based feature structures. The range of syntactic phenomena that can be handled is large and includes auxiliaries (including inversion), copula, raising and small clause constructions, topicalization, relative clauses, infinitives, gerunds, passives, adjuncts, it-clefts, wh-clefts, PRO constructions, noun-noun modifications, extraposition, determiner sequences, genitives, negation, noun-verb contractions, sentential adjuncts and imperatives. This technical report corresponds to the XTAG Release 8/31/98. The XTAG grammar is continuously updated with the addition of new analyses and modification of old ones, and an online version of this report can be found at the XTAG web page at http://www.cis.upenn.edu/~xtag/Comment: 310 pages, 181 Postscript figures, uses 11pt, psfig.te

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

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 triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the triple structure recommended for annotations in these works (which is isomorphic to the triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag’s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag’s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag’s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag’s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag’s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag’s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger’s schema, a concrete instance of OntoTag’s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely • Bitext’s DataLexica (http://www.bitext.com/EN/datalexica.asp), • LACELL’s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), • Connexor’s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and • EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag’s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger’s configuration, a concrete instance of OntoTag’s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL’s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger’s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG’S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. • CONFIRMED by the development of: o OntoTag’s annotation scheme, o OntoTag’s annotation architecture, o OntoTagger’s (XML, RDF, OWL) annotation schemas, o OntoTagger’s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. • CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. • H.3 was CONFIRMED by means of the development of OntoTagger’s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor’s FDG, Bitext’s DataLexica and LACELL’s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). • CONFIRMED by means of the development of OntoTagger’s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. • CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. • REJECTED: OntoTagger’s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag’s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag’s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag’s linguistic ontologies). • On the one hand, OntoTag’s network of ontologies consists of − The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; − The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; − The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; − The OIO (OntoTag’s Integration Ontology), which Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO; Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. • On the other hand, OntoTag’s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: − As far as morphosyntactic annotations are concerned, OntoTag’s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; − As for syntactic annotations, OntoTag’s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; − Regarding semantic annotations, OntoTag’s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; − The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag’s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL’s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually pe