Automatic acquisition of hyponyms from large text corpora
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An important and well-studied problem is the production of semantic lexicons from a large corpus. In this paper, we present a system named ASIA (Automatic Set Instance Acquirer), which takes in the name of a semantic class as input (e.g., "car makers") and automatically outputs its instances (e.g., "ford", "nissan", "toyota"). ASIA is based on recent advances in web-based set expansion - the problem of finding all instances of a set given a small number of "seed" instances. This approach effectively exploits web resources and can be easily adapted to different languages. In brief, we use language-dependent hyponym patterns to find a noisy set of initial seeds, and then use a state-of-the-art language-independent set expansion system to expand these seeds. The proposed approach matches or outperforms prior systems on several English-language benchmarks. It also shows excellent performance on three dozen additional benchmark problems from English, Chinese and Japanese, thus demonstrating language-independence.