1,424 research outputs found
Learning Moore Machines from Input-Output Traces
The problem of learning automata from example traces (but no equivalence or
membership queries) is fundamental in automata learning theory and practice. In
this paper we study this problem for finite state machines with inputs and
outputs, and in particular for Moore machines. We develop three algorithms for
solving this problem: (1) the PTAP algorithm, which transforms a set of
input-output traces into an incomplete Moore machine and then completes the
machine with self-loops; (2) the PRPNI algorithm, which uses the well-known
RPNI algorithm for automata learning to learn a product of automata encoding a
Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore
machine using PTAP extended with state merging. We prove that MooreMI has the
fundamental identification in the limit property. We also compare the
algorithms experimentally in terms of the size of the learned machine and
several notions of accuracy, introduced in this paper. Finally, we compare with
OSTIA, an algorithm that learns a more general class of transducers, and find
that OSTIA generally does not learn a Moore machine, even when fed with a
characteristic sample
Semantic Parsing with Bayesian Tree Transducers
Many semantic parsing models use tree transformations to map between natural language and meaning representation. However, while tree transformations are central to several state-of-the-art approaches, little use has been made of the rich literature on tree automata. This paper makes the connection concrete with a tree transducer based semantic parsing model and suggests that other models can be interpreted in a similar framework, increasing the generality of their contributions. In particular, this paper further introduces a variational Bayesian inference algorithm that is applicable to a wide class of tree transducers, producing state-of-the-art semantic parsing results while remaining applicable to any domain employing probabilistic tree transducers.9 page(s
Learning probability distributions generated by finite-state machines
We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference
in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft
: Méthodes d'Inférence Symbolique pour les Bases de Données
This dissertation is a summary of a line of research, that I wasactively involved in, on learning in databases from examples. Thisresearch focused on traditional as well as novel database models andlanguages for querying, transforming, and describing the schema of adatabase. In case of schemas our contributions involve proposing anoriginal languages for the emerging data models of Unordered XML andRDF. We have studied learning from examples of schemas for UnorderedXML, schemas for RDF, twig queries for XML, join queries forrelational databases, and XML transformations defined with a novelmodel of tree-to-word transducers.Investigating learnability of the proposed languages required us toexamine closely a number of their fundamental properties, often ofindependent interest, including normal forms, minimization,containment and equivalence, consistency of a set of examples, andfinite characterizability. Good understanding of these propertiesallowed us to devise learning algorithms that explore a possibly largesearch space with the help of a diligently designed set ofgeneralization operations in search of an appropriate solution.Learning (or inference) is a problem that has two parameters: theprecise class of languages we wish to infer and the type of input thatthe user can provide. We focused on the setting where the user inputconsists of positive examples i.e., elements that belong to the goallanguage, and negative examples i.e., elements that do not belong tothe goal language. In general using both negative and positiveexamples allows to learn richer classes of goal languages than usingpositive examples alone. However, using negative examples is oftendifficult because together with positive examples they may cause thesearch space to take a very complex shape and its exploration may turnout to be computationally challenging.Ce mĂ©moire est une courte prĂ©sentation dâune direction de recherche, Ă laquelle jâai activementparticipĂ©, sur lâapprentissage pour les bases de donnĂ©es Ă partir dâexemples. Cette recherchesâest concentrĂ©e sur les modĂšles et les langages, aussi bien traditionnels quâĂ©mergents, pourlâinterrogation, la transformation et la description du schĂ©ma dâune base de donnĂ©es. Concernantles schĂ©mas, nos contributions consistent en plusieurs langages de schĂ©mas pour les nouveaumodĂšles de bases de donnĂ©es que sont XML non-ordonnĂ© et RDF. Nous avons ainsi Ă©tudiĂ©lâapprentissage Ă partir dâexemples des schĂ©mas pour XML non-ordonnĂ©, des schĂ©mas pour RDF,des requĂȘtes twig pour XML, les requĂȘtes de jointure pour bases de donnĂ©es relationnelles et lestransformations XML dĂ©finies par un nouveau modĂšle de transducteurs arbre-Ă -mot.Pour explorer si les langages proposĂ©s peuvent ĂȘtre appris, nous avons Ă©tĂ© obligĂ©s dâexaminerde prĂšs un certain nombre de leurs propriĂ©tĂ©s fondamentales, souvent souvent intĂ©ressantespar elles-mĂȘmes, y compris les formes normales, la minimisation, lâinclusion et lâĂ©quivalence, lacohĂ©rence dâun ensemble dâexemples et la caractĂ©risation finie. Une bonne comprĂ©hension de cespropriĂ©tĂ©s nous a permis de concevoir des algorithmes dâapprentissage qui explorent un espace derecherche potentiellement trĂšs vaste grĂące Ă un ensemble dâopĂ©rations de gĂ©nĂ©ralisation adaptĂ© Ă la recherche dâune solution appropriĂ©e.Lâapprentissage (ou lâinfĂ©rence) est un problĂšme Ă deux paramĂštres : la classe prĂ©cise delangage que nous souhaitons infĂ©rer et le type dâinformations que lâutilisateur peut fournir. Nousnous sommes placĂ©s dans le cas oĂč lâutilisateur fournit des exemples positifs, câest-Ă -dire desĂ©lĂ©ments qui appartiennent au langage cible, ainsi que des exemples nĂ©gatifs, câest-Ă -dire qui nâenfont pas partie. En gĂ©nĂ©ral lâutilisation Ă la fois dâexemples positifs et nĂ©gatifs permet dâapprendredes classes de langages plus riches que lâutilisation uniquement dâexemples positifs. Toutefois,lâutilisation des exemples nĂ©gatifs est souvent difficile parce que les exemples positifs et nĂ©gatifspeuvent rendre la forme de lâespace de recherche trĂšs complexe, et par consĂ©quent, son explorationinfaisable
Joint Morphological and Syntactic Disambiguation
In morphologically rich languages, should morphological and syntactic disambiguation be treated sequentially or as a single problem? We describe several efficient, probabilistically interpretable ways to apply joint inference to morphological and syntactic disambiguation using lattice parsing. Joint inference is shown to compare favorably to pipeline parsing methods across a variety of component models. State-of-the-art performance on Hebrew Treebank parsing is demonstrated using the new method. The benefits of joint inference are modest with the current component models, but appear to increase as components themselves improve
Schema-Guided Induction of Monadic Queries
International audienceThe induction of monadic node selecting queries from partially annotated XML-trees is a key task in Web information extraction. We show how to integrate schema guidance into an RPNI-based learning algorithm, in which monadic queries are represented by pruning node selecting tree transducers. We present experimental results on schema guidance by the DTD of HTML
- âŠ