The corpus-based approach has indeed been quite successful in producing comprehensive, moderately accurate speech recognizers, part-of-speech (POS) taggers, parsers for learned probabilistic phrase-structure grammars, and even MT and text-based QA systems and summarization systems. However, semantic processing has been restricted to rather shallow aspects, such as extraction of specific data concerning specific kinds of events from text (e.g., location, date, perpetrators, victims, etc., of terrorist bombings) or extraction of clusters of argument types, relational tuples, or paraphrase sets from text corpora. Currently, the corpus-based, statistical approaches are still dominant, but there appears to be a growing movement towards integration of formal logical approaches to language with corpus-based statistical approaches in order to achieve deeper understanding and more intelligent behavior in language comprehension and dialogue systems. There are also efforts to combine connectionist and neural-net approaches with symbolic and logical ones. The following sections will elaborate on many of the topics touched on above. General references for computational linguistics are Allen 1995,  Jurafsky and Martin 2009, and Clark et al. 2010.