Choh Man Teng

Actual causation: a stone soup essay

AbstractWe argue that current discussions of criteria for actual causation are ill-posed in several respects. (1) The methodology of current discussions is by induction from intuitions about an infinitesimal fraction of the possible examples and counterexamples; (2) cases with larger numbers of causes generate novel puzzles; (3) “neuron” and causal Bayes net diagrams are, as deployed in discussions of actual causation, almost always ambiguous; (4) actual causation is (intuitively) relative to an initial system state since state changes are relevant, but most current accounts ignore state changes through time; (5) more generally, there is no reason to think that philosophical judgements about these sorts of cases are normative; but (6) there is a dearth of relevant psychological research that bears on whether various philosophical accounts are descriptive. Our skepticism is not directed towards the possibility of a correct account of actual causation; rather, we argue that standard methods will not lead to such an account. A different approach is required.nOnce upon a time a hungry wanderer came into a village. He filled an iron cauldronnwith water, built a fire under it, and dropped a stone into the water. “I do like a tasty stone soup” he announced. Soon a villager added a cabbage to the pot, another added some salt and others added potatoes, onions, carrots, mushrooms, and so on, until there was a meal for all.

Complex Event Extraction using DRUM

Complex mechanisms, such as cell-signaling pathways, consist of many highly interconnected components, yet they are often described in disconnected fragmentary ways. The goal of DRUM (Deep Reader for Understanding Mechanisms) is to develop a system that can read papers and combine results of individual studies into a comprehensive explanatory model. A first step is to automatically extract relevant events and event relationships from the literature. This paper describes initial steps in extending an existing general deep language understanding system, TRIPS, to read biomedical papers. In a preliminary evaluation, our system was the best performing system among the participants, achieving results close to human expert performance. These results suggested that our system is viable for complex event extraction and, ultimately, understanding complex systems and mechanisms.

Compositionality in Verb-Particle Constructions

We are developing a broad-coverage deep semantic lexicon for a system that parses sentences into a logical form expressed in a rich ontology that supports reasoning. In this paper we look at verb-particle constructions (VPCs), and the extent to which they can be treated compositionally vs idiomatically. First we distinguish between the different types of VPCs based on their compositionality and then present a set of heuristics for classifying specific instances as compositional or not. We then identify a small set of general sense classes for particles when used compositionally and discuss the resulting lexical representations that are being added to the lexicon. By treating VPCs as compositional whenever possible, we attain broad coverage in a compact way, and also enable interpretations of novel VPC usages not explicitly present in the lexicon.