A critique of the loose definitions of primitives and means of combination in Adele Goldberg's book on Construction Grammar. Cites van Zaanen (2000), Clark (2001), Klein and Manning (2002), Steels (2004), Dennis (2005), Zuidema (2006), Seginer (2007) as well as own work, particularly "From exemplar to grammar..."

A central problem in grounded language acqui- sition is learning the correspondences between a rich world state and a stream of text which refer- ences that world state. To deal with the high de- gree of ambiguity present in this setting, we present a generative model that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state. We show that our model generalizes across three domains of increasing difficulty—Robocup sportscasting, weather forecasts (a new domain), and NFL recaps.

Not quite sure what they are doing here. They describe everything in geoquery but give results in biomedical? They take dependency parse as given but induce paraphrases?

Machine learning offers a range of tools for training systems from data, but these methods are only as good as the underly- ing representation. This paper proposes to acquire representations for machine learn- ing by reading text written to accommo- date human learning. We propose a novel form of semantic analysis called read- ing to learn, where the goal is to obtain a high-level semantic abstract of multi- ple documents in a representation that fa- cilitates learning. We obtain this abstract through a generative model that requires no labeled data, instead leveraging repe- tition across multiple documents. The se- mantic abstract is converted into a trans- formed feature space for learning, result- ing in improved generalization on a rela- tional learning task.