Adjectives are words that describe or modify other elements in a sentence. As such, they are frequently used to convey facts and opinions about the nouns they modify. Connecting nouns to the corresponding adjectives becomes vital for intelligent tasks such as aspect-level sentiment analysis or interpretation of complex queries (e.g., "small hotel with large rooms") for fine-grained information retrieval. To respond to the need, we propose a methodology that identifies dependencies of nouns and adjectives by looking at syntactic clues related to part-of-speech sequences that help recognize such relationships. These sequences are generalized into patterns that are used to train a binary classifier using machine learning methods. The capabilities of the new method are demonstrated in two, syntactically different languages: English, the leading language of international discourse, and Hebrew, whose rich morphology poses additional challenges for parsing. In each language we compare our method with a designated, state-of-the-art parser and show that it performs similarly in terms of accuracy while: (a) our method uses a simple and relatively small training set; (b) it does not require a language specific adaptation, and (c) it is robust across a variety of writing styles.