Selective Sampling for Example-based Word Sense Disambiguation

TL;DR

This paper introduces a selective sampling method for example-based word sense disambiguation that reduces manual supervision and search time by choosing the most informative examples based on their training utility.

Contribution

The paper presents a novel utility-based sampling approach that efficiently constructs a smaller, effective example set for disambiguation systems, improving efficiency without sacrificing accuracy.

Findings

Reduced supervision and search overheads in experiments

Maintained system performance with smaller example sets

Effective sampling method validated on about one thousand sentences

Abstract

This paper proposes an efficient example sampling method for example-based word sense disambiguation systems. To construct a database of practical size, a considerable overhead for manual sense disambiguation (overhead for supervision) is required. In addition, the time complexity of searching a large-sized database poses a considerable problem (overhead for search). To counter these problems, our method selectively samples a smaller-sized effective subset from a given example set for use in word sense disambiguation. Our method is characterized by the reliance on the notion of training utility: the degree to which each example is informative for future example sampling when used for the training of the system. The system progressively collects examples by selecting those with greatest utility. The paper reports the effectiveness of our method through experiments on about one thousand sentences. Compared to experiments with other example sampling methods, our method reduced both the overhead for supervision and the overhead for search, without the degeneration of the performance of the system.