Copestake, Ann; Herbelot, Aurélie
Computer Laboratory, University of Cambridge, aac10@cl.cam.ac.uk
Computer Laboratory, University of Cambridge, ah433@cl.cam.ac.uk
Synonymy in an approach to combined distributional and compositional semantics
We outline a perspective on synonymy from a novel approach to combining distributional and compositional semantics, Lexicalized Compositionality (LC).  In LC logical forms, the standard model-theoretic lexical predicates (e.g., catʹ) are replaced with distributional concepts (notated as e.g., cat°).  cat° corresponds to the set of all linguistic contexts (expressed as logical forms) in which the lexeme cat occurs.  We consider hypothetical contexts, which are based on all the possible statements true in some world (allowing a comparison to model-theoretic approaches), and actual contexts, corresponding to an individual's linguistic experience.
The aim of LC is to investigate models of semantics which are psycholinguistically plausible in terms of linguistic information. Some utterances are directly grounded.  In such cases, contexts can be paired with perceptual data, and linguistic concepts, such as cat°, related directly to real world entities.  However utterances can be understood which are not immediately grounded at all or only partially grounded.  We assume that speakers' knowledge of word meaning is primarily acquired implicitly, from actual contexts (grounded or ungrounded), but that there is also a more minor role for explicit `expert' knowledge.  Individual speakers will have somewhat different models of word meaning, and understanding may thus require negotiation.
Within this approach, the notion of synonymy is multifaceted.  Classic synonymy corresponds to the situation where a hearer is (more or less) directly informed that a term means the same thing as one they are already familiar with.  For example:
The aubergine (eggplant) has to be one of my favourite vegetables. http://www.earth.li/~kake/cookery/aubergine.html (last accessed 10th August 2010)
The much commoner case of near-synonymy (Edmonds and Hirst, 2002) corresponds to similarities between context sets.  We hypothesise that the process of understanding a new word involves an initial approximation to the nearest known linguistic concepts(s) (by context set comparison), followed by the acquisition of differentiating information.  We define a notion of a characteristic context in terms of the differences between two context sets. For instance, a hearer to whom rancid is a relatively unfamiliar term may initially relate it to off/bad/rotten etc, but then additionally differentiate it using further contexts (perhaps by its preferential application to fatty foodstuffs compared to bad).  Alternatively, or additionally, an `expert' definition may be known.  In normal communication, the coarse-grained notion of synonymy (i.e., that rancid relates to off etc) usually suffices for understanding. 
We should emphasize the differences between our approach and the usual computational models of distributional similarity (Harper, 1965, onwards).  Clearly our contexts are much more structured: the recent trend is towards higher structure (e.g., Erk and Padó (2008)), but our work is still distinct in aiming for a logical account that incorporates distributions, thus allowing for (relatively conventional) logical inference.  Furthermore, in our account, the contexts come from a specific individual's experience.  We expect this to give very different results from the standard approaches which utilise data from very heterogeneous sources.
Our work is at a very early stage and has primarily involved development of the theoretical account.  Given the current lack of individualised corpora, empirical work involves approximations of linguistic contexts.  However, we believe these ideas suggest new theoretical perspectives for computational experiments on synonymy.
Bibliography 
Edmonds, Philip and Graeme Hirst (2002) ‘Near-synonymy and lexical choice’, Computational Linguistics, 28(2), 105–144.
 Erk, Katrin and Sebastian Padó (2008) ‘A Structured Vector Space Model for Word Meaning in Context’, Proceedings of the EMNLP, Honolulu, HI.
Harper, K.E. (1965) ‘Measurement of similarity between nouns’, The RAND Corporation, Santa Monica.