State-of-the-Art Kernels for Natural Language Processing

In recent years, machine learning (ML) has been used more and more to solve complex tasks in dif- ferent disciplines, ranging from Data Mining to In- formation Retrieval or Natural Language Processing (NLP). These tasks often require the processing of structured input, e.g., the ability to extract salient features from syntactic/semantic structures is criti- cal to many NLP systems. Mapping such structured data into explicit feature vectors for ML algorithms requires large expertise, intuition and deep knowl- edge about the target linguistic phenomena. Ker- nel Methods (KM) are powerful ML tools (see e.g., (Shawe-Taylor and Cristianini, 2004)), which can al- leviate the data representation problem. They substi- tute feature-based similarities with similarity func- tions, i.e., kernels, directly defined between train- ing/test instances, e.g., syntactic trees. Hence fea- ture vectors are not needed any longer. Additionally, kernel engineering, i.e., the composition or adapta- tion of several prototype kernels, facilitates the de- sign of effective similarities required for new tasks, e.g., (Moschitti, 2004; Moschitti, 2008).