Radu Gramatovici

Parsing with P Automata

P automata are membrane systems that work as accepting devices. In this chapter, two parsing methods using P automata are presented. The first method uses P automata with active membranes for parsing natural language sentences into dependency trees. The second method uses a variant of P automata with evolution and communication rules for parsing Marcus contextual languages.

Parsing with Active P Automata

New classes of P automata are introduced corresponding to the basic classes of languages in the Chomsky hierarchy. Unlike the previously defined P automata, active P automata are computing with the structure of the membrane systems, using operations like membrane creation, division and dissolution. The model is applied to the parsing of (natural language) sentences into dependency trees.

An Efficient Parser for a Class of Contextual Languages

A class of Marcus contextual languages containing non-context-free languages is defined. A parser for this class of languages is developed. The parser uses an operatorial automaton and it works in square time in the length of the input words.

Sorted dependency insertion grammars

An enhanced generative formalism is proposed based on the combination of two features: contextual derivation (as in Marcus contextual grammars) and sorted dependency structures (as in dependency grammars). The model is related to a variant of restarting automaton with rewriting and deletion. Preliminary results on the generative power as well as closure and decidability properties of the new model are presented.

Tiling Rectangular Pictures with P Systems

We introduce a class of two-dimensional tiling languages which are distinct from the local and recognizable picture languages. These languages arise from tilings of the integer lattice Z 2. For such picture languages, we define a class of tissue-like P systems with active membranes as a generative device. We also make a comparison with the model introduced in [2,3] for the generation of local and recognizable two-dimensional languages, using a similar type of P systems.

Syllabic Languages and Go-through Automata

In this paper we define a new class of contextual grammars and study how the languages generated by such grammars can be accepted by go-through automata. The newly introduced class of grammars is a generalization of the formalism previously used to describe the linguistic process of syllabification. Go-through automata which are used here to recognize, and also parse, the languages generated by this new class of grammars are generalizations of push-down automata in the area of context-sensitivity; they have been proved to be an efficient tool for the recognition of languages generated by contextual grammars. The main results of the paper show how the newly introduced generative model is related with other classes of Marcus contextual languages, and how syllabic languages are recognized and parsed using go-through automata.

Contextual Multilanguages: A Learning Method

In this paper, we deal with a class of contextual languages defined in the frame of order-sorted algebra. We define a new way to describe contextual (multi)languages, using constraints. For this class of constraint contextual languages a learning method with motivations from natural language processing is provided.

k-local internal contextual grammars

In this paper we propose a generalization of the local internal contextual grammars, introduced by Ilie in 1997, namely the k-local internal contextual grammars. These grammars are, in fact, classical internal contextual grammars, but their only permitted derivations are those that can be described in a restricted manner (that depends on the number k). Using this formalism we define different classes of languages, and obtain a series of language theoretic results for them. Also, we show that the membership problem for k-local internal contextual grammars with polynomial choice can be solved in polynomial time. This seems interesting to us, as it shows that the descriptional restrictions on the derivations of a grammar reflect on the computational efficiency of accepting the language generated by that grammar.