Extracting Semantic Roles from a Model of Eventualities(2)

The notion of semantic roles is usually at-tributed to Fillmore [8], however its history can be traced back through TesniSre [16] to Panini. Following this tradition, many researchers rec-ognize their usefulness in the description of language-- even if the

iteria and through an identification of all the participants in i0. Our first step toward this abstraction was to consider each participant (individuals or properties) either as a localized entity (a token) or a location (a place), and to determine its role in the realization of the process expressed by the predicate. The model exhibits some c o m m o n points with a localist approach [1,11] since it recognizes (in an abstract sense) the importance of spatio-temporal "regions" in the process of individuation of events [14]. To express the change of localization (again in an abstract sense), the notion of transitions is used. The entire construction is inspired by Petri net theory [15]: a set S of places, a set T of transitions, a flow relation F: (S x T) ~ (T x S) and markers are the categories used to define the structure of a process (and as a consequence of the events composing it). For example, the dynamic representation of Max embarque la caisse sur le cargo [3J/Max embarks the crate on the cargo boat can be analyzed in two steps. First there is a transition from an initial state IS where the crate is not on the cargo boat to a final state FS where the crate is on the cargo boat. The final state can be expressed by the static passive, la caisse est embarqude sur le cargo~the crate was embarked on the cargo boat, and is schematized in (2). One of the argument (cargo boat) is used as a localization while the other argument is used as a localized entity (crate), the THEME according to Gruber [9]. The initial state can be expressed (in this case) by the negation of the final state and is schematized in (1). The realization of the entire process is then represented by the firing of the net which can be illustrated by the snapshots (1) and (2). 1. Is:t~ir-~O:Fs 2. IS:O---[---(~):Fs To integrate the participation of "Max" in the model, we recognize the importance of335causality in the discrimination of events [13,14]. Since the cause is understood to be the first entity responsible for the realization of events [6], the obvious schematization is (3). 3. 4. It is possible that a recursive definition (places and transitions) will be necessary to express "properly" the causation, the localization of events and processes or the concept of dynamic states [2,14]. In that case, the schematization could then be (4). But we can achieve the same result through a proper type definition of the transition expressing the cause: (s x 0 -~ (t x ((s x t) -, (t x s))), where "s" is a place and "t", a transition. This approach to semantic roles determination is close to the one undertook by Jackendoff [12]. His identification of each role to a particular argument position in a conceptual relation is given here by the way it participate to the firing of the net. (It is our guess that most of the conceptual relations used by Jackendoff can be expressed within this model, giving to them an operational interpretation.) The model has the advantage to give an explicit