Pre-meeting - Daniel

Focus paper: Unsupervised Ontology Induction from Text

Related paper: Unsupervised Semantic Parsing

This paper presents the USP system, upon which the system of the focus paper is based. The authors aim to create an fully unsupervised system capable of parsing text to a deep semantic representation. Instead of trying to learn both syntax and semantics at the same time, this system takes syntactic parses as given and induces semantics from them. The first stage in the process is a deterministic mapping from the given dependency tree to a quasi-logical form: essentially just a first-order logical representation of the dependency tree. Then, the goal of the semantic parsing is to partition these representations into separate lambda forms, and cluster lambda forms that have the same meaning. Training the model is done using Markov Logic Networks, and parsing is done using a greedy search through the space of possible partitions of the QLF.

Since there is no clear way to evaluate an an unsupervised semantic parser intrinsically, the authors apply their system to the task of question answering. They restrict their attention to the biomedical domain, and compare to other open-domain question answering systems, and they perform above the other systems. In addition, they manually inspect the outputs of their system, and claim that the semantic clusters produced tend to be very coherent and that their system is capable of understanding many different types of paraphrases.