The availability of large, syntactically-bracketed corpora such as the Penn Tree Bank affords us the opportunity to automatically build or train broad-coverage grammars, and in particular to train probabilistic grammars. A number of recent parsing experiments have also indicated that grammars whose production probabilities are dependent on the context can be more effective than context-free grammars in selecting a correct parse. To make maximal use of context, we have automatically constructed, from the Penn Tree Bank version 2, a grammar in which the symbols S and NP are the only real nonterminals, and the other non-terminals or grammatical nodes are in effect embedded into the right-hand-sides of the S and NP rules. For example, one of the rules extracted from the tree bank would be S -? NP VBX JJ CC VBX NP [1] (where NP is a non-terminal and the other symbols are terminals -- part-of-speech tags of the Tree Bank). The most common structure in the Tree Bank associated with this expan...

A number of theoretical studies have been devoted to the notion of sublanguage, which mainly concerns linguistic phenomena restricted by the domain or context. Furthermore, there are some successful NLP systems which have explicitly or implicitly addressed the sublanguage restrictions (e.g. TAUM-METEO, ATR). This suggests the following two objectives for future NLP research: 1) automatic linguistic knowledge acquisition for sublanguage, and 2) automatic definition of sublanguage and identification of it for a new text. The two issues become realistic owing to the appearance of large corpora. Despite of the recent bloom of the research on the first objective, there are few on the second objective. If this objective is achieved, NLP systems will be able to optimize to the sublanguage before processing the text, and this will be a significant help in automatic processing. A preliminary experiment aiming at the second objective is addressed in this paper. It is conducted on about 3 MB of W...