Selective Sampling for Example-based Word Sense Disambiguation

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.

25 pages, 14 Postscript figures

References in corpus (3)

• Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
• Exemplar-Based Word Sense Disambiguation: Some Recent Improvements
• Sequential Model Selection for Word Sense Disambiguation

Cited by in corpus (15)

• Naive Bayes and Exemplar-Based approaches to Word Sense Disambiguation Revisited
• Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems
• SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs
• Active Learning of Discriminative Subgraph Patterns for API Misuse Detection
• Exploring Connections Between Active Learning and Model Extraction
• Boosting Applied to Word Sense Disambiguation
• Regional based query in graph active learning
• Minimum-Margin Active Learning
• Feature Space Exploration For Planning Initial Benthic AUV Surveys
• Sampling Bias in Deep Active Classification: An Empirical Study
• NeuroDB: A Neural Network Framework for Answering Range Aggregate Queries and Beyond
• A Comparison between Supervised Learning Algorithms for Word Sense Disambiguation
• ALEX: Active Learning based Enhancement of a Model's Explainability
• Active Learning in Incomplete Label Multiple Instance Multiple Label Learning
• Convergence of Nearest Neighbor Pattern Classification with Selective Sampling