Careful abstraction from instance families in memory-based language learning

Empirical studies in inductive language learning point at pure memory-based learning as a successful approach to many language learning tasks, often performing better than lerning methods that abstract from the learning material. The possibility is left open, however, that limited, careful abstraction in memory-based learning may be harmless to generalisation, as long as the disjunctivity of language data is preserved. We compare three types of careful abstraction: editing, oblivious (partial) decision-tree abstraction, and generalised instances, in a single-task study. Only when combined with feature weighting, careful abstraction can equal pure memory-based learning. In a multi-task case study we nd that the fambl algorithm, a new careful abstractor which merges families of instances, performs close to pure memory-based learning, though it equals it only on one task. On the basis of the gathered empirical results, we argue for the incorporation of the notion of instance families, i.e., carefully generalised instances, in memory-based language learning.