In this paper authors will show how verb valency data, added to the Croatian dictionary in NooJ, enhances recognition of VP as well as NP and PP parts of a sentence. At the Department of Information Sciences two parallel PhD Theses were being developed. One is construction of a chunker for Croatian using NooJ and the other one is construction of Croatian verb valency lexicon (CRVLLEX). Here we combined the two projects by adding the data from CRVLLEX to the existing NooJ dictionary hoping thus to obtain better, improved Croatian chunker results. Our dictionary has over 36 000 entries of which 1 884 are verbs. Each verb is only marked by its category and the FLX. Additional data is being added from the CRVLLEX. Theoretic motivation behind the construction of CRVLLEX is Praha’s Dependency Treebank (PDT) but also good Czech verb valency dictionaries (VALLEX and Verbalex). So far, CRVLLEX has 1 739 verbs with 5 118 valency frames (approx. 3 frames per verb) and 173 syntactic-semantic classes. Each word entry in CRVLLEX contains headword lemma, reflexivity example (depending whether the verb is reflexive or not) and frame entry which is the part that describes the valency frame for each verb. Every verb also has the following attributes: aspect, frequency and form, which we hope will be of great importance in disambiguating parts of a sentence surrounding the verb.