We describe the architecture we set up during the SANCL shared task for parsing usergenerated texts, that deviate in various ways from linguistic conventions used in available training treebanks. This architecture focuses in coping with such a divergence. It relies on the PCFG-LA framework (Petrov and Klein, 2007), as implemented by Attia et al. (2010). We explore several techniques to augment robustness: (i) a lexical bridge technique (Candito et al., 2011) that uses unsupervised word clustering (Koo et al., 2008); (ii) a special instanciation of self-training aimed at coping with POS tags unknown to the training set; (iii) the wrapping of a POS tagger with rulebased processing for dealing with recurrent non-standard tokens; and (iv) the guiding of out-of-domain parsing with predicted part-ofspeech tags for unknown words and unknown (word, tag) pairs. Our systems ranked second and third out of eight in the constituency parsing track of the SANCL competition. 1