LAFLang 2013 Abstracts


Full Papers
Paper Nr: 1
Title:

Contribution of Probabilistic Grammar Inference with k-Testable Language for Knowledge Modeling - Application on Aging People

Authors:

Catherine Combes and Jean Azéma

Abstract: We investigate the contribution of unsupervised learning and regular grammatical inference to respectively identify profiles of elderly people and their development over time in order to evaluate care needs (human, financial and physical resources). The proposed approach is based on k-Testable Languages in the Strict Sense Inference algorithm in order to infer a probabilistic automaton from which a Markovian model which has a discrete (finite or countable) state-space has been deduced. In simulating the corresponding Markov chain model, it is possible to obtain information on population ageing. We have verified if our observed system conforms to a unique long term state vector, called the stationary distribution and the steady-state.

Paper Nr: 2
Title:

Linguistic Applications of Finite Automata with Translucent Letters

Authors:

Benedek Nagy and László Kovács

Abstract: Finite automata with translucent letters do not read their input strictly from left to right as traditional finite automata, but for each internal state of such a device, certain letters are translucent, that is, in this state the automaton cannot see them. We address the word problem of these automata, both in the deterministic and in the nondeterministic case. Some interesting examples from the formal language theory and from a segment of the Hungarian language is shown using automata with translucent letters.

Paper Nr: 3
Title:

Learning, Agents and Formal Languages - State of the Art

Authors:

Leonor Becerra Bonache and M. Dolores Jiménez-López

Abstract: The paper presents the state of the art of machine learning, agent technologies and formal languages, not considering them as isolated research areas, but focusing on the relationship among them. First, we consider the relationship between learning and agents. Second, the relationship between machine learning and formal languages. And third, the relationship between agents and formal languages. Finally, we point to some promising directions on the intersection among these three areas.

Paper Nr: 4
Title:

Learning Probabilistic Subsequential Transducers from Positive Data

Authors:

Hasan Ibne Akram and Colin de la Higuera

Abstract: In this paper we present a novel algorithm for learning probabilistic subsequential transducers from a randomly drawn sample. We formalize the properties of the training data that are sufficient conditions for the learning algorithm to infer the correct machine. Finally, we report experimental evidences to backup the correctness of our proposed algorithm.

Paper Nr: 5
Title:

Different Approaches for Development Tools for Natural Computers - Grammar Driven vs. Model Driven Approaches

Authors:

David Fernández, Francisco Saiz, Marina de la Cruz and Alfonso Ortega

Abstract: In this paper we will compare our first steps in two different approaches to define programming languages for NEPs (one bio-inspired model of computation in which our research group is interested). The classic approach proposed by the literature several decades ago is focused on the grammar of the syntax of the language being defined. Recently the focus is moved to a formal description (model) of the model of computation for which the programming language is being designed. This approach is called model driven. The designer simply adds syntax, semantics checks and translation routines to the different elements of the model that are applied. Programming is usually understood as instantiating the model. After introducing the main characteristics of each model for this particular case some conclusions and further research tasks are discussed.

Paper Nr: 7
Title:

Situated Agents in Linguistic Contexts

Authors:

Roussanka Loukanova

Abstract: The paper looks at motivations from interdisciplinary applications for some of the major situation-theoretical objects. We present potential applications of situation theory to computational semantics by introducing situational modelling of linguistic contexts and agents such as speakers and listeners in context.