Apprentissage automatique de la morphologie : le cas des structures racine-schème

RÉSUMÉ Cette thèse porte sur le développement de méthodes algorithmiques pour découvrir automatiquement la structure morphologique des mots d'un corpus. On considère en particulier le cas des langues s'approchant du type introflexionnel, comme l'arabe ou l'hébreu. La tradition linguistique décrit la morphologie de ces langues en termes d'unités discontinues : les racines consonantiques et les schèmes vocaliques. Ce genre de structure constitue un défi pour les systèmes actuels d'apprentissage automatique, qui opèrent généralement avec des unités continues. La stratégie adoptée ici consiste à traiter le problème comme une séquence de deux sous-problèmes. Le premier est d'ordre phonologique : il s'agit de diviser les symboles (phonèmes, lettres) du corpus en deux groupes correspondant autant que possible aux consonnes et voyelles phonétiques. Le second est de nature morphologique et repose sur les résultats du premier : il s'agit d'établir l'inventaire des racines et schèmes du corpus et de déterminer leurs règles de combinaison. On examine la portée et les limites d'une approche basée sur deux hypothèses : (i) la distinction entre consonnes et voyelles peut être inférée sur la base de leur tendance à alterner dans la chaîne parlée; (ii) les racines et les schèmes peuvent être identifiés respectivement aux séquences de consonnes et voyelles découvertes précédemment. L'algorithme proposé utilise une méthode purement distributionnelle pour partitionner les symboles du corpus. Puis il applique des principes analogiques pour identifier un ensemble de candidats sérieux au titre de racine ou de schème, et pour élargir progressivement cet ensemble. Cette extension est soumise à une procédure d'évaluation basée sur le principe de la longueur de description minimale, dans- l'esprit de LINGUISTICA (Goldsmith, 2001). L'algorithme est implémenté sous la forme d'un programme informatique nommé ARABICA, et évalué sur un corpus de noms arabes, du point de vue de sa capacité à décrire le système du pluriel. Cette étude montre que des structures linguistiques complexes peuvent être découvertes en ne faisant qu'un minimum d'hypothèses a priori sur les phénomènes considérés. Elle illustre la synergie possible entre des mécanismes d'apprentissage portant sur des niveaux de description linguistique distincts, et cherche à déterminer quand et pourquoi cette coopération échoue. Elle conclut que la tension entre l'universalité de la distinction consonnes-voyelles et la spécificité de la structuration racine-schème est cruciale pour expliquer les forces et les faiblesses d'une telle approche. ABSTRACT This dissertation is concerned with the development of algorithmic methods for the unsupervised learning of natural language morphology, using a symbolically transcribed wordlist. It focuses on the case of languages approaching the introflectional type, such as Arabic or Hebrew. The morphology of such languages is traditionally described in terms of discontinuous units: consonantal roots and vocalic patterns. Inferring this kind of structure is a challenging task for current unsupervised learning systems, which generally operate with continuous units. In this study, the problem of learning root-and-pattern morphology is divided into a phonological and a morphological subproblem. The phonological component of the analysis seeks to partition the symbols of a corpus (phonemes, letters) into two subsets that correspond well with the phonetic definition of consonants and vowels; building around this result, the morphological component attempts to establish the list of roots and patterns in the corpus, and to infer the rules that govern their combinations. We assess the extent to which this can be done on the basis of two hypotheses: (i) the distinction between consonants and vowels can be learned by observing their tendency to alternate in speech; (ii) roots and patterns can be identified as sequences of the previously discovered consonants and vowels respectively. The proposed algorithm uses a purely distributional method for partitioning symbols. Then it applies analogical principles to identify a preliminary set of reliable roots and patterns, and gradually enlarge it. This extension process is guided by an evaluation procedure based on the minimum description length principle, in line with the approach to morphological learning embodied in LINGUISTICA (Goldsmith, 2001). The algorithm is implemented as a computer program named ARABICA; it is evaluated with regard to its ability to account for the system of plural formation in a corpus of Arabic nouns. This thesis shows that complex linguistic structures can be discovered without recourse to a rich set of a priori hypotheses about the phenomena under consideration. It illustrates the possible synergy between learning mechanisms operating at distinct levels of linguistic description, and attempts to determine where and why such a cooperation fails. It concludes that the tension between the universality of the consonant-vowel distinction and the specificity of root-and-pattern structure is crucial for understanding the advantages and weaknesses of this approach.

Strategic Adaptation in Function of Chronicity of Schizophrenic Symptoms During A Fluency Task: An fMRI Study

Background: Language processing abnormalities and executive difficulties are hallmark features of schizophrenia. The objective of this study is to assess the blood oxygenation level-dependent (BOLD) response at two different stages of the illness (i.e. comparison between adolescents and adults with schizophrenic symptoms) during a fluency task.Methods: BOLD responses during a covert verbal fluency task were compared between 11 psychotic adolescents with schizophrenic symptoms (mean age 16,9 years) and 14 adults with schizophrenia (mean age 33,4 years). fMRI data were analyzed with standard routine of spm5.Results: First, expected activation's network was found for both groups, separately. Secondly, adolescents showed greater activation in left rolandic opercule (BA 48), left angular (BA 39) and right hippocampus compared to adults. Thirdly, adults demonstrated greater activation in presupplementary motor area (BA 6) and in precentral area (BA 4) compared to adolescents.Conclusions: The adolescents seemed to recruit a verbal network (Broca and Wernicke) and memory abilities to perform a fluency task. In contrast, adults seemed to recruit more executive function abilities to perform a similar task. Despite the evolution of schizophrenia, which is known to have a deleterious influence on the prefrontal cortex development, adult patients seemed to be able to recruit such areas to perform a verbal fluency / executive function task.

The influence of tobacco consumption on the relationship between schizotypy and hemispheric asymmetry.

Tobacco use is positively associated with severity of symptoms along the schizophrenia spectrum. Accordingly it could be argued that neuropsychological performance, formerly thought to be modulated by schizotypy, is actually modulated by drug use or an interaction of drug use and schizotypy. We tested whether habitual cigarette smokers as compared to non-smokers would show a neuropsychological profile similar to that observed along the schizophrenia spectrum and, if so, whether smoking status or nicotine dependence would be more significant modulators of behavior than schizotypy. Because hemispheric dominance has been found to be attenuated along the schizophrenia spectrum, 40 right-handed male students (20 non-smokers) performed lateralized left- (lexical decisions) and right- (facial decision task) hemisphere dominant tasks. All individuals completed self-report measures of schizotypy and nicotine dependence. Schizotypy predicted laterality in addition to smoking status: While positive schizotypy (Unusual Experiences) was unrelated to hemispheric performance, Cognitive Disorganization predicted reduced left hemisphere dominant language functions. These latter findings suggest that Cognitive Disorganization should be regarded separately as a potentially important mediator of thought disorganization and language processing. Additionally, increasing nicotine dependence among smokers predicted a right hemisphere shift of function in both tasks that supports the role of the right hemisphere in compulsive/impulsive behavior.

Llengua al límit. Una llengua, dos camins. L'encontre necessari.

En el treball es realitza una transcripció de dos programes de televisió, amb la idea de saber quin és el tipus de llenguatge que usen aquests mitjans per adreçar-se al seu públic. Però seria absurd ignorar altres canals per als quals la llengua és imprescindible. Em refereixo al cinema, sobretot. I malgrat que no es considera un mitjà de comunicació, també és un element importantíssim pel que fa al tractament i transmissió lingüístics. I molts productes del cinema acaben sortint per televisió. La premsa escrita i, com a cas especial, Internet, també hi tenen força a dir.

Decreased pyramidal neuron size in Brodmann areas 44 and 45 in patients with autism.

Autism is a neurodevelopmental disorder characterized by deficits in social interaction and social communication, as well as by the presence of repetitive and stereotyped behaviors and interests. Brodmann areas 44 and 45 in the inferior frontal cortex, which are involved in language processing, imitation function, and sociality processing networks, have been implicated in this complex disorder. Using a stereologic approach, this study aims to explore the presence of neuropathological differences in areas 44 and 45 in patients with autism compared to age- and hemisphere-matched controls. Based on previous evidence in the fusiform gyrus, we expected to find a decrease in the number and size of pyramidal neurons as well as an increase in volume of layers III, V, and VI in patients with autism. We observed significantly smaller pyramidal neurons in patients with autism compared to controls, although there was no difference in pyramidal neuron numbers or layer volumes. The reduced pyramidal neuron size suggests that a certain degree of dysfunction of areas 44 and 45 plays a role in the pathology of autism. Our results also support previous studies that have shown specific cellular neuropathology in autism with regionally specific reduction in neuron size, and provide further evidence for the possible involvement of the mirror neuron system, as well as impairment of neuronal networks relevant to communication and social behaviors, in this disorder.

Semantiikan hyödyntäminen dokumenttienhallinnassa

Työn tavoitteena on etsiä asiakasyritykselle sähköteknisen dokumentoinnin hallintaan sopiva järjestelmäratkaisu vertailemalla insinööritoimistojen käyttämien suunnittelujärjestelmien ja yleisten dokumenttien hallintajärjestelmien soveltuvuutta asiakasympäristöön. Työssä tutkitaan sopivien metatietojen kuvaustapojen käyttökelpoisuutta sähköteknisen dokumentoinnin hallintaan esimerkkiprojektien avulla. Työn sisältö koostuu neljästä pääkohdasta. Ensimmäisessä jaksossa tarkastellaan dokumentin ominaisuuksia ja elinkaarta luonnista aktiivikäyttöön, arkistointiin ja hävitykseen. Samassa yhteydessä kerrotaan dokumenttienhallinnan perustehtävistä. Toisessa jaksossa käsitellään dokumenttien kuvailun tavoitteita, kuvailusuosituksia ja -standardeja sekä luonnollisen kielen käyttöä sisällönkuvailussa. Tarkastelukohteina suosituksista ovat W3C:n julkaisemat suositukset, Dublin Core, JHS 143 ja SFS-EN 82045. Kolmannessa jaksossa tarkastellaan teollisuuden dokumentoinnin ominaispiirteitä ja käyttötarkoitusta. Teollisuudessa on monia erilaisia järjestelmäympäristöjä tehtaan sisällä ja työssä kuvataan dokumenttienhallinnan integrointitarpeita muihin järjestelmiin. Viimeisessä jaksossa kuvaillaan erilaisia dokumentoinnin hallintaympäristöjä alkaen järeimmästä päästä tuotetiedon hallintajärjestelmistä siirtyen pienempiinsuunnittelujärjestelmiin ja lopuksi yleisiin dokumenttien hallintajärjestelmiin. Tässä osassa on myös luettelo ohjelmistotoimittajista. Työn tuloksena on laadittu valituista dokumenttityypeistä metatietokuvaukset kahden eri kuvaustavan (JHS 143 ja SFS-EN 82045) avulla ja on todettu molemmat kuvaustavat käyttökelpoisiksi sähköteknisen dokumentoinnin käsittelyyn.Nämä kuvaukset palvelevat asiakasta dokumenttienhallintaprojektin määrittelytyössä. Asiakkaalle on tehty myös vertailu sopivista järjestelmävaihtoehdoista hankintaa varten.

Heterogeenisen palveluverkon kuvaus ja käyttö XML-kielen avulla

Tämän diplomityön tarkoituksena on käydä läpi XML:n tarjoamia mahdollisuuksia heterogeenisen palveluverkon integroinnissa. Työssä kuvataan XML-kielen yleistä teoriaa ja perehdytään etenkin sovellusten välisen kommunikoinnin kannalta tärkeisiin ominaisuuksiin. Samalla käydään läpi sovelluskehitysympäristöjen muuttumista heterogeenisemmiksi ja siitä seurannutta palveluarkkitehtuurien kehittymistä ja kuinka nämä muutokset vaikuttavat XML:n hyväksikäyttöön. Työssä suunniteltiin ja toteutettiin luonnollisen kielen palvelukehitykseen Fuse-palvelualusta. Työssä kuvataan palvelualustan arkkitehtuuri ja siinä tarkastellaan XML:n hyödyntämistä luonnollisen kielen tulkin ja palvelun integroinnissa. Samalla arvioidaan muita XML:n käyttömahdollisuuksia Fuse-palvelualustan parantamiseksi.

Source separation techniques applied to linear prediction

The prediction filters are well known models for signal estimation, in communications, control and many others areas. The classical method for deriving linear prediction coding (LPC) filters is often based on the minimization of a mean square error (MSE). Consequently, second order statistics are only required, but the estimation is only optimal if the residue is independent and identically distributed (iid) Gaussian. In this paper, we derive the ML estimate of the prediction filter. Relationships with robust estimation of auto-regressive (AR) processes, with blind deconvolution and with source separation based on mutual information minimization are then detailed. The algorithm, based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics. Experimental results emphasize on the interest of this approach.

Imaging bilinguals: When the neurosciences meet the languange sciences

The starting point of our investigation was the longstanding notion that bilingual individuals need effective mechanisms to prevent interference from one language while processing material in the other (e.g. Penfield and Roberts, 1959). To demonstrate how the prevention of interference is implemented in the brain we employed event-related brain potentials (ERPs; see Munte, Urbach, ¨ Duzel and Kutas, 2000, for an introductory review) ¨ and functional magnetic resonance imaging (fMRI) techniques, thus pursuing a combined temporal and spatial imaging approach. In contrast to previous investigations using neuroimaging techniques in bilinguals, which had been mainly concerned with the localization of the primary and secondary languages (e.g. Perani, Paulesu, Galles, Dupoux, Dehaene, Bettinardi, Cappa, Fazio and Mehler, 1998; Chee, Caplan, Soon, Sriram, Tan, Thiel and Weekes, 1999), our study addressed the dynamic aspects of bilingual language processing.

Functional neuroanatomy of meaning acquisition from context

An important issue in language learning is how new words are integrated in the brain representations that sustain language processing. To identify the brain regions involved in meaning acquisition and word learning, we conducted a functional magnetic resonance imaging study. Young participants were required to deduce the meaning of a novel word presented within increasingly constrained sentence contexts that were read silently during the scanning session. Inconsistent contexts were also presented in which no meaning could be assigned to the novel word. Participants showed meaning acquisition in the consistent but not in the inconsistent condition. A distributed brain network was identified comprising the left anterior inferior frontal gyrus (BA 45), the middle temporal gyrus (BA 21), the parahippocampal gyrus, and several subcortical structures (the thalamus and the striatum). Drawing on previous neuroimaging evidence, we tentatively identify the roles of these brain areas in the retrieval, selection, and encoding of the meaning.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Tekijännimet Mikael Agricolan teosten kielessä. Henkilötarkoitteisten johdosten merkitykset, funktiot ja rakenteet

My research deals with agent nouns in the language of the works of Mikael Agricola (ca. 1510–1557). The main tasks addressed in my thesis have been to describe individual agent noun types, to provide a comprehensive picture of the category of agent nouns and to clarify the relations between different types of agent nouns. My research material consists of all the agent nouns referring to persons in the language of Agricola’s works, together with their context. The language studied is for the most part translated language. Agent nouns play an important role both in the vocabulary of natural language and in broader sentence structures, since in a text it is constantly necessary to refer to actors re-ferring to persons in the text. As a concept and a phenomenon, the agent noun is widely known in languages. It is a word formed with a certain derivational affixes, which typical-ly refers to a person. In my research the agent noun category includes both deverbal and denominal derivatives referring to persons, e.g. kirjoittaa > kirjoittaja (to write > writer), asua > asuva (to inhabit > inhabitant), imeä > imeväinen (to suck > suckling), juopua > juopunut (to drink > drunkard), pelätä > pelkuri (to fear > one who fears ‘a coward’), apu > apulainen (help/to help > helper); lammas > lampuri (sheep > shepherd). Besides original Finnish expressions, agent noun derivatives taken as such from foreign languages form a word group of central importance for the research (e.g. nikkari, porvari, ryöväri, based on the German/Swedish for carpenter, burgher, robber). Especially important for the formation of agent nouns in Finnish are the models offered by foreign languages. The starting point for my work is predominantly semantic, as both the criteria for collecting the material and the categorisation underlying the analysis of the material are based on semantic criteria. When examining derivatives, aspects relating to structure are also inevitably of central importance, as form and meaning are closely associated with each other in this type of vocabulary. The alliance of structure and meaning can be described in an illustrative manner with the help of structural schemata. The examination of agent nouns comprises on the one hand analysis of syntactic elements and on the other, study of cultural words in their most typical form. The latter aspect offers a research object in which language and the extralinguistic world, referents, their designations and cultural-historical reality are in concrete terms one and the same. Thus both the agent noun types that follow the word formation principles of the Finn-ish language and those of foreign origin borrowed as a whole into Finnish illustrate very well how an expression of a certain origin and formed according to a certain structural model is inseparably bound up with the background of its referent and in general with semantic factors. This becomes evident both on the level of the connection between cer-tain linguistic features and text genre and in relation to cultural words referring to per-sons. For example, the model for the designations of God based on agent nouns goes back thousands of years and is still closely linked in 16th century literature with certain text genres. This brings out the link between the linguistic feature and the genre in a very con-crete manner. A good example of the connection between language and the extralinguistic world is provided by the cultural vocabulary referring to persons. Originally Finnish agent noun derivatives are associated with an agrarian society, while the vocabulary relat-ing to mediaeval urbanisation, the Hansa trade and specialisation by trade or profession is borrowed and originates in its entirety from vocabulary that was originally German.

Linguistic models for decision support

Linguistic modelling is a rather new branch of mathematics that is still undergoing rapid development. It is closely related to fuzzy set theory and fuzzy logic, but knowledge and experience from other fields of mathematics, as well as other fields of science including linguistics and behavioral sciences, is also necessary to build appropriate mathematical models. This topic has received considerable attention as it provides tools for mathematical representation of the most common means of human communication - natural language. Adding a natural language level to mathematical models can provide an interface between the mathematical representation of the modelled system and the user of the model - one that is sufficiently easy to use and understand, but yet conveys all the information necessary to avoid misinterpretations. It is, however, not a trivial task and the link between the linguistic and computational level of such models has to be established and maintained properly during the whole modelling process. In this thesis, we focus on the relationship between the linguistic and the mathematical level of decision support models. We discuss several important issues concerning the mathematical representation of meaning of linguistic expressions, their transformation into the language of mathematics and the retranslation of mathematical outputs back into natural language. In the first part of the thesis, our view of the linguistic modelling for decision support is presented and the main guidelines for building linguistic models for real-life decision support that are the basis of our modeling methodology are outlined. From the theoretical point of view, the issues of representation of meaning of linguistic terms, computations with these representations and the retranslation process back into the linguistic level (linguistic approximation) are studied in this part of the thesis. We focus on the reasonability of operations with the meanings of linguistic terms, the correspondence of the linguistic and mathematical level of the models and on proper presentation of appropriate outputs. We also discuss several issues concerning the ethical aspects of decision support - particularly the loss of meaning due to the transformation of mathematical outputs into natural language and the issue or responsibility for the final decisions. In the second part several case studies of real-life problems are presented. These provide background and necessary context and motivation for the mathematical results and models presented in this part. A linguistic decision support model for disaster management is presented here – formulated as a fuzzy linear programming problem and a heuristic solution to it is proposed. Uncertainty of outputs, expert knowledge concerning disaster response practice and the necessity of obtaining outputs that are easy to interpret (and available in very short time) are reflected in the design of the model. Saaty’s analytic hierarchy process (AHP) is considered in two case studies - first in the context of the evaluation of works of art, where a weak consistency condition is introduced and an adaptation of AHP for large matrices of preference intensities is presented. The second AHP case-study deals with the fuzzified version of AHP and its use for evaluation purposes – particularly the integration of peer-review into the evaluation of R&D outputs is considered. In the context of HR management, we present a fuzzy rule based evaluation model (academic faculty evaluation is considered) constructed to provide outputs that do not require linguistic approximation and are easily transformed into graphical information. This is achieved by designing a specific form of fuzzy inference. Finally the last case study is from the area of humanities - psychological diagnostics is considered and a linguistic fuzzy model for the interpretation of outputs of multidimensional questionnaires is suggested. The issue of the quality of data in mathematical classification models is also studied here. A modification of the receiver operating characteristics (ROC) method is presented to reflect variable quality of data instances in the validation set during classifier performance assessment. Twelve publications on which the author participated are appended as a third part of this thesis. These summarize the mathematical results and provide a closer insight into the issues of the practicalapplications that are considered in the second part of the thesis.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.