Case is an abstract grammatical feature that indicates argument relationship in a sentence. In English, cases are expressed on pronouns, as nominative case (e.g. I, he), accusative case (e.g. me, him) and genitive case (e.g. my, his). Children correctly use cased pronouns at a very young age. How do they acquire abstract case in the first place, when different cases are not associated with different meanings? This paper proposes that the distributional patterns in parents’ input could be used to distinguish grammatical cases in English.

This work lays the foundation for automated assessments of narrative quality in student writing. We first manually score essays for narrative-relevant traits and sub-traits, and measure inter-annotator agreement. We then explore linguistic features that are indicative of good narrative writing and use them to build an automated scoring system. Experiments show that our features are more effective in scoring specific aspects of narrative quality than a state-of-the-art feature set.

This paper reviews annotation schemes used for labeling discourse coherence in well-formed and noisy (essay) data, and it describes a system that we have developed for automated holistic scoring of essay coherence. We review previous, related work on unsupervised computational approaches to evaluating discourse coherence and focus on a taxonomy of discourse coherence schemes classified by their different goals and types of data. We illustrate how a holistic approach can be successfully used to build systems for noisy essay data, across domains and populations. We discuss the model features related to human scoring guide criteria for essay scoring, and the importance of using model features relevant to these criteria for the purpose of generating meaningful scores and feedback for students and test-takers. To demonstrate the effectiveness of a holistic annotation scheme, we present results of system evaluations.