This paper compares two techniques for robust parsing of extra-grammatical natural language that might be of interest in large scale Textual Data Analysis applications. The first one returns a "correct" derivation for any extra-grammatical sentence by generating the finest corresponding most probable optimal maximum coverage. The second one extends the initial grammar by adding relaxed grammar rules in a controlled manner. Both techniques use a stochastic parser that selects a "best" solution among multiple analyses. The techniques were tested on the ATIS and Susanne corp ora and exp erimental results, as well as conclusions on performance comparison, are provided.