Natural languages like English have been constrained in expressing perceptions like vision, sound and touch for years despite the efforts of Joyce (1922, 1939) and others. In situ, lexicons have been limited in their form and content. They have typically been structured in the form of sequences of natural language words with their content defined using flat symbolic descriptions in natural languages. In particular, we believe that today's dictionaries in general, and with respect to Artificial Intelligence (AI) systems in particular, are unnatural in the sense that they do not encode pictures for words just like we do in our heads. There is now a move towards integrated systems in Artificial Intelligence (AI) (see Dennett 1991; Mc Kevitt 1994a, 1994b) and that will cause a need for dire actions on lexical research in the form of integrated lexicons. We believe that lexicons must move towards a situation where natural language words are also defined in terms of spatial and visual structures. These spatial and visual structures will solve what have been two of the most prominent problems in the field of Natural Language Processing (NLP) for years: (1) Where are symbolic semantic primitive meanings in computer programs grounded? and (2) how come some words, typically in the defining vocabulary, in dictionaries have circular definitions so that words end up defining each other? We believe integrated lexicons will cause these two problems to go away and hence help solve Searle's Chinese Room Problem and move more towards Irish Rooms of people like James Joyce.