Bubble Networks: A Context-Sensitive Lexicon Representation

The paradigm of packing semantics into words has long been the dogma of lexical semantics. As computational semantics is maturing as a field, we are beginning to see the failure of this paradigm. Pustejovsky’s Generative Lexicon is only able to have generative powers if the semantics packed within a word can anticipate possible combinations with other words. However, even an idealized Generative Lexicon will have to grapple with the effects of implicit, non-lexical context (e.g. topics, commonsense knowledge) on meaning determination. In this paper, we propose an intrinsic, connectionist network representation of a lexicon called a bubble network. In a bubble network, meaning is a result of graph traversal, from some word-concept node toward a context (e.g. “wedding” in the context of “ritual”), or toward another lexical item (e.g. “fast car”). Possible meanings are disambiguated using a modified spreading activation function which incorporates ideas of structural message-passing and active contexts. An encapsulation mechanism allows larger lexical expressions and assertional knowledge to be incorporated into the network, and along with the notion of utility-based learning of weights, helps to give a more natural account of lexical evolution (acquisition, deletion, generalization, individuation). A preliminary implementation and evaluation of bubble networks show that lexical expressions can be interpreted with remarkable contextsensitivity, but also point to some pragmatic problems in lexicon building using a bubble network.