122 research outputs found

    A Reformulation of Matrix Graph Grammars with Boolean Complexes

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    Prior publication in the Electronic Journal of Combinatorics.Graph transformation is concerned with the manipulation of graphs by means of rules. Graph grammars have been traditionally studied using techniques from category theory. In previous works, we introduced Matrix Graph Grammars (MGG) as a purely algebraic approach for the study of graph dynamics, based on the representation of simple graphs by means of their adjacency matrices. The observation that, in addition to positive information, a rule implicitly defines negative conditions for its application (edges cannot become dangling, and cannot be added twice as we work with simple digraphs) has led to a representation of graphs as two matrices encoding positive and negative information. Using this representation, we have reformulated the main concepts in MGGs, while we have introduced other new ideas. In particular, we present (i) a new formulation of productions together with an abstraction of them (so called swaps), (ii) the notion of coherence, which checks whether a production sequence can be potentially applied, (iii) the minimal graph enabling the applicability of a sequence, and (iv) the conditions for compatibility of sequences (lack of dangling edges) and G-congruence (whether two sequences have the same minimal initial graph).This work has been partially sponsored by the Spanish Ministry of Science and Innovation, project METEORIC (TIN2008-02081/TIN)

    Matrix Graph Grammars

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    This book objective is to develop an algebraization of graph grammars. Equivalently, we study graph dynamics. From the point of view of a computer scientist, graph grammars are a natural generalization of Chomsky grammars for which a purely algebraic approach does not exist up to now. A Chomsky (or string) grammar is, roughly speaking, a precise description of a formal language (which in essence is a set of strings). On a more discrete mathematical style, it can be said that graph grammars -- Matrix Graph Grammars in particular -- study dynamics of graphs. Ideally, this algebraization would enforce our understanding of grammars in general, providing new analysis techniques and generalizations of concepts, problems and results known so far.Comment: 321 pages, 75 figures. This book has is publisehd by VDM verlag, ISBN 978-363921255

    Utilisation de langages formels pour la modélisation et la résolution de problèmes de planification de quarts de travail

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    Résumé La planification d'horaires de personnel représente un défi pour plusieurs organisations. Dans cette thèse, nous étudions différentes variantes du problème de planification des quarts de travail. La planification des quarts de travail représente la sélection d'un ensemble de quarts couvrant une période de temps, typiquement une journée à une semaine, divisée en périodes de durées égales pour lesquelles un nombre d'employés requis est donné. Un quart est défini par son heure de début, sa durée et par sa composition en termes d'affectation de pauses et d'activités de travail. Nous divisons les problèmes de planification de quarts en quatre classes. Premièrement, les problèmes où les employés sont considérés comme identiques et les problèmes où les employés ont des caractéristiques individuelles qui les distinguent et qui doivent être prises en compte dans la sélection des quarts. Nous référons à la première classe de problèmes comme étant des problèmes anonymes et à la seconde, comme étant des problèmes personnalisés. Ensuite, nous différencions les problèmes de planification de quarts mono-activité et multi-activités. La première classe de problèmes est en fait un cas particulier de la deuxième. Elle détermine quelles périodes du quart sont affectées à des activités de travail et quelles périodes sont affectées à des activités de repos (repos, pause, repas), alors que les problèmes de planification de quarts multi-activités définissent pour chaque période de travail du quart quelle activité de travail, parmi un ensemble d'activités de travail pouvant être effectuées, lui est affectée. Dans ce cas, pour chaque activité de travail et pour chaque période de temps sur lequel s'étend l'horaire, le nombre d'employés requis est donné. Dans cette thèse, nous abordons chacune des classes de problèmes de planification de quarts à l'aide de différentes approches, toutes basées sur la formulation des contraintes restreignant la formation des quarts par des outils issus de la théorie des langages formels. En effet, à l'aide d'automates et de grammaires, nous pouvons définir des langages qui sont composés d'un ensemble de mots représentant des quarts respectant les contraintes de notre problème. Les automates et les grammaires sont des outils très expressifs qui permettent de formuler plusieurs concepts relatifs à notre contexte de manière relativement naturelle. Plus précisément, notre première contribution présente comment, à partir d'un automate ou d'une grammaire définissant les règles régissant la composition des quarts pour un problème, nous pouvons générer automatiquement un modèle linéaire en nombres entiers basé sur des variables d'affectation binaires dans une structure de graphe qui encode tous les quarts permis par ce problème. La transformation d'un automate ou d'une grammaire en modèle de programmation mathématique est inspirée de structures de la programmation par contraintes. Un exemple experimental de problème de planification de quarts multi-activités montre la puissance de la modélisation utilisant les langages formels. En effet, cette nouvelle approche de modélisation permet d'aborder des règles très complexes en ce qui concerne la composition des quarts multi-activités. Malgré que l'intérêt de ce type de modèles dans le contexte de problèmes de planification de quarts soit indéniable, les résultats expérimentaux soulèvent les limites de la formulation décomposée par employés et définie avec des variables d'affectation binaires. En effet, avec le nombre d'employés et le nombre d'activités de travail qui augmentent, le modèle devient difficile à résoudre directement.---------- Abstract Personnel scheduling is a challenging problem for many organizations. In this thesis, we address different versions of the shift scheduling problem. The shift scheduling problem is to select a set of shifts to cover a planning horizon, typically from 1 to 7 days, divided into periods of equal length for which the required numbers of employees are given. A shift is defined by its starting time, its duration and its composition. Its composition is defined by the position of the breaks and work activities within the shift. We divide the shift scheduling problems into four main classes. First, we distinguish the problems where the employees are considered to be identical from the problems where each employee has individual characteristics that must be taken into account when assigning them to shifts. We call the first class the anonymous problems and the second one, the personalized problems. Then, we differenciate two other classes of problems, the mono-activity and the multi-activity shift scheduling problems. The former is a particular case of the latter and consists in specifying the work and rest periods to assign to the shifts. In the multi-activity case, we are also interested in a set of distinct work-activities to be performed. So, not only should we specify if the shift is assigned to a work-activity or not at a given period, but we must specify to which work-activity it is assigned. In this case, for each work-activity and each period, the required number of employees is given. In this thesis, we study each of these classes of shift scheduling problems with different approaches where constraints on shift construction are formulated with tools based on formal languages. In fact, using automata and grammars, we define languages composed of words that represent allowed shifts for our problem. More precisely, our first contribution presents how, from a finite automata or a context-free grammar defining the rules constraining the construction of shifts for a given problem, one can automatically generate an integer programming model based on binary assignment variables in a graph structure embedding every allowed shift for this problem. The transformation of an automaton or a grammar into a mathematical programming model is inspired by structures from constraint programming. Experiments on a multi-activity shift scheduling problem show that formal language based modeling is very powerful and allows us to address complex rules in the construction of multi-activity shifts. However, while the relevance of our modeling approach is clear, the experimental results reveal some limitations on the scalability of the formulation based on binary assignment variables and decomposed on employees. In fact, when the number of employees and the number of work-activities grow, the model is hard to solve directly

    Specification theory : the treatment of redundancy in generative phonology

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    Meaning versus Grammar

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    This volume investigates the complicated relationship between grammar, computation, and meaning in natural languages. It details conditions under which meaning-driven processing of natural language is feasible, discusses an operational and accessible implementation of the grammatical cycle for Dutch, and offers analyses of a number of further conjectures about constituency and entailment in natural language

    Term selection in information retrieval

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    Systems trained on linguistically annotated data achieve strong performance for many language processing tasks. This encourages the idea that annotations can improve any language processing task if applied in the right way. However, despite widespread acceptance and availability of highly accurate parsing software, it is not clear that ad hoc information retrieval (IR) techniques using annotated documents and requests consistently improve search performance compared to techniques that use no linguistic knowledge. In many cases, retrieval gains made using language processing components, such as part-of-speech tagging and head-dependent relations, are offset by significant negative effects. This results in a minimal positive, or even negative, overall impact for linguistically motivated approaches compared to approaches that do not use any syntactic or domain knowledge. In some cases, it may be that syntax does not reveal anything of practical importance about document relevance. Yet without a convincing explanation for why linguistic annotations fail in IR, the intuitive appeal of search systems that ‘understand’ text can result in the repeated application, and mis-application, of language processing to enhance search performance. This dissertation investigates whether linguistics can improve the selection of query terms by better modelling the alignment process between natural language requests and search queries. It is the most comprehensive work on the utility of linguistic methods in IR to date. Term selection in this work focuses on identification of informative query terms of 1-3 words that both represent the semantics of a request and discriminate between relevant and non-relevant documents. Approaches to word association are discussed with respect to linguistic principles, and evaluated with respect to semantic characterization and discriminative ability. Analysis is organised around three theories of language that emphasize different structures for the identification of terms: phrase structure theory, dependency theory and lexicalism. The structures identified by these theories play distinctive roles in the organisation of language. Evidence is presented regarding the value of different methods of word association based on these structures, and the effect of method and term combinations. Two highly effective, novel methods for the selection of terms from verbose queries are also proposed and evaluated. The first method focuses on the semantic phenomenon of ellipsis with a discriminative filter that leverages diverse text features. The second method exploits a term ranking algorithm, PhRank, that uses no linguistic information and relies on a network model of query context. The latter focuses queries so that 1-5 terms in an unweighted model achieve better retrieval effectiveness than weighted IR models that use up to 30 terms. In addition, unlike models that use a weighted distribution of terms or subqueries, the concise terms identified by PhRank are interpretable by users. Evaluation with newswire and web collections demonstrates that PhRank-based query reformulation significantly improves performance of verbose queries up to 14% compared to highly competitive IR models, and is at least as good for short, keyword queries with the same models. Results illustrate that linguistic processing may help with the selection of word associations but does not necessarily translate into improved IR performance. Statistical methods are necessary to overcome the limits of syntactic parsing and word adjacency measures for ad hoc IR. As a result, probabilistic frameworks that discover, and make use of, many forms of linguistic evidence may deliver small improvements in IR effectiveness, but methods that use simple features can be substantially more efficient and equally, or more, effective. Various explanations for this finding are suggested, including the probabilistic nature of grammatical categories, a lack of homomorphism between syntax and semantics, the impact of lexical relations, variability in collection data, and systemic effects in language systems

    A theory of word order in categorial grammar with special reference to Spanish.

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