Underspecification Of 'Meaning': The Case Of Russian Imperfective Aspect

One main problem for NLP applications is that natural language expressions are underspecified and require enrichments of different sorts to get a truthconditional interpretaton in context. Underspecification applies on two levels: what is said underdetermines what is meant, and linguistic meaning underspecifies what is said. One instance of this phenomenon is aspect in Russian, especially the imperfective one. It gives rise to a variety of readings, which are difficult to capture by one invariant meaning. Instead, the imperfective aspect is sense-general; its meaning has to be specified in the course of interpretation by contextual cues and pragmatic inferences. This paper advocates an account of the different imperfective readings in terms of pragmatic principles and inferential heuristics based on, and supplied by, a semantic skeleton consisting of a ‘selectional theory’ of aspect. This framework might serve as basis for a rule-guided derivation of aspectual readings in Russian. 1 Linguistic underspecification Natural language expressions deliver merely templates for the construction of a contextually relevant interpretation, namely the propositional meaning the hearer ascribes to the respective perceived utterance. What is linguistically given delivers only part of the input necessary for interpretation and has to be enriched and specified by recourse to sentence-level and discourse-level context, as well as to world-knowledge. Propositions, i.e. truth-conditional content, arise only after that enrichment process has taken place. NLP applications need to capture the meaning of a linguistic input to adequately work with it in the respective applications. But the ‘meaning’ relevant in this case, viz. the intended interpretation of an utterance, is underspecified by the lexically given input in at least two ways. Linguistic meaning underspecifies propositions, i.e. ‘what is said’ (Carston, 2002), and ‘what is said’ in turn underspecifies ‘what is meant’ (Grice, 1989). Both kinds of underspecification have to be solved by the hearer in natural discourse settings, and by the corresonding algorithms in NLP-applications. As such applications necessarily rely on linguistically given input, they have to be supplemented by a systematic account of the inferential mechanisms needed to enrich and specify the lexical schemes. A further difficulty for NLP applications is that, presuming some form of cooperativity, any utterance can be interpreted by appropriately accommodating the context. Apparently, computing the contextually relevant interpretation of an utterance requires more than capturing and computing lexical input. It relies crucially on further information and principles of how to derive that information and combine it with what is lexically given. Information – contextual or conceptual – that is not contained in the natural language string currently being processed is subject to pragmatic actions (Perrett, 2000: 102). This paper is organized as follows: Section 2 illustrates the assumptions on pragmatic reasoning relevant here. Section 3 deals with Russian ipf aspect as one example of linguistic underspecification and sketches the semantics and pragmatics involved in the derivation of its readings. Implications for NLP will be given in section 4, and section 5 offers a short conclusion. 2 Pragmatic Reasoning The twofold underspecification of natural language expressions is resolved by pragmatic reasoning: ‘implicatures’, derived by inference alone, enrich what is said to what is meant and ‘explicatures’, derived by decoding plus inference, enrich the lexical input to what is said. Both interact in mutual parallel adjustment, in that the expectation of certain implicatures constrains the possible range of explicatures. Thus, we are dealing with two kinds of semantics – lexical and propositonal – and with two kinds of pragmatics – explicature and implicature (Carston, 2002): Figure 1. Interaction of Semantics and Pragmatics The pragmatic actions can be captured by Levinson’s (2000) heuristics, whose application gives rise to default interpretations by evoking certain inferences. The heuristics and their corresponding inferences are based on Grice’s Maxims of Conversation: Q-heuristics are based on the first quantity maxim (‘make your statement as informative as possible’) and license inference to the negation or invalidity of a corresponding stronger expression, M-heuristics stem from violations of the manner maxim (esp. ‘avoid obscurity of expression’ and ‘avoid prolixity’), and license the inference from marked expressions to marked interpretations. I-heuristics are based on the second quantity maxim (‘do not say more than necessary’) and allow for inference to a stereotype. Contrary to the Gricean view, however, these are assumed to work partly also on the subpropositional level giving rise to ‘explicatures’ (Carston, 2002), thereby enriching and constraining the underspecified lexical representation. An advantage of using heuristics giving rise to default interpretations is that they capture the fact that real minds have to make decisions under constraints of limited time and knowledge. In hand-ling tasks like interpretation, humans have to use approximate methods like heuristics that guide the search and determine when it should end, and simple decision rules that make use of the information found. To behave adaptively in the environment in general, and in interpreting utterances in special, humans must be able to make inferences that are fast, frugal and accurate (Gigerenzer et al., 1999). These inferences, or the heuristics they are based on, respectively, work well insofar as they make a tradeoff on the dimension of generality vs. specificity. Their simplicity allows them to be robust when confronted with a change in environment and to generalize to new situations. Such heuristics are rather suitable means for dealing with underspecified, sense-general linguistic expressions that equally make a tradeoff between underdetermination and preciseness. Furthermore, heuristics are of the kind NLP applications can deal with (section 4). Levinson’s heuristics, at least the Mand Qheuristics, are instances of such fast and frugal heuristics that give rise to default inferences which have to be made more specific in the course of interpretation. This is achieved by the rather specific I-heuristics, that give rise to inferences by referring to assumptions provided by a concept being activated by a lexical item in a certain context and discourse setting. 3 Ipf aspect in Russian One instance of underspecification is aspect in Russian, especially the ipf, which gives rise to a considerable variety of readings (1a-g). Their context-dependence and defeasibility indicates their partial pragmatic character. 1a. actual-processual reading Šar medlenno podnimalsja. balloon slowly ascend:PAST:ipf ‘The balloon was ascending slowly.’ 1b. inactual reading Ran ́še on rabotal v universitete. in the past he work:PAST:ipf at university:LOC. ‘He used to work as a teacher.’ 1c. general-factual reading Vot na ėtoj stene visela kartina. there at that wall:LOChang:PAST:ipf painting. ‘There was a painting hanging on that wall.’ 1d. durative reading Ona dolgo smotrela na fotografii. she for a long time look:PAST:ipf at photographs:ACC ‘She looked at the photos for a long time.’ 1e. atemporal reading Železo tonet v vode. iron go down:PAST:ipf in water:LOC ‘Iron goes down in water.’ 1 ‘ipf’ = (Russian) imperfective aspect; ‘pf’ = (Russian) perfective aspect 3 2 Logical form