An Experiment with Theme-Rheme Identification

In this paper we start from the theory of Functional Sentence Perspective developed primarily by Firbas [1], Svoboda [12] and also later by Sgall et al. [9]. We make an attempt to formulate and implement a procedure for Czech allowing to automatically recognize which sentence constituents carry information that is contextually dependent and thus known to an addressee (theme), constituents containing new information (rheme), and also constituents bearing non-thematic and non-rhematic information (transition). The experimental implementation of the procedure uses tools developed in NLP Centre, FI MU, particularly the morphological analyzer Majka [17], disambiguator DESAMB [16] and parser SET [5]. As a starting data resource we use a small corpus of 120 Czech sentences, which at the moment does not include a free continuous text. This is motivated by the fact that we do not use syntactically pre-tagged text but perform syntactic analysis directly using the parser SET. Thus, we offer only a very basic evaluation, which captures the main FSP phenomena and shows that the task is feasible. The toolset developed for the experiment consists of two parts: first, a chunker, which determines word-order positions from the parse tree of a sentence, second, an FSP tagger which is the implementation of the procedure. It labels the chunks with the tags of what is further called functional elements (e.g. theme proper, transition, rheme proper). An experimental version is available at http://nlp. fi.muni.cz/~xsvobo15/fsp/fsp.html.