As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently process it and extract different types of knowledge from it. My research goal in this thesis is to develop learning models that can automatically induce representations of human language, in particular its structure and meaning in order to solve multiple higher level language tasks.
There has been great progress in delivering technologies in natural language processing such as extracting information, sentiment analysis or grammatical analysis. However, solutions are often based on different machine learning models. My goal is the development of general and scalable algorithms that can jointly solve such tasks and learn the necessary intermediate representations of the linguistic units involved. Furthermore, most standard approaches make strong simplifying language assumptions and require well designed feature representations. The models in this thesis address these two shortcomings. They provide effective and general representations for sentences without assuming word order independence. Furthermore, they provide state of the art performance with no, or few manually designed features.