Are human-input seeds good enough for entity set expansion? Seeds rewriting by leveraging Wikipedia semantic knowledge
|Are human-input seeds good enough for entity set expansion? Seeds rewriting by leveraging Wikipedia semantic knowledge|
|Author(s)||Qi Z., Liu K., Zhao J.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Information extraction, Seed rewrite, Semantic knowledge (Extra: High quality, Information Extraction, Seed set, Semantic class, Semantic knowledge, Semantic relatedness, Set expansions, Web resources, Wikipedia, Information retrieval, Infrared devices, Natural language processing systems, Semantics, Websites, Knowledge management)|
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Are human-input seeds good enough for entity set expansion? Seeds rewriting by leveraging Wikipedia semantic knowledge is a 2012 conference paper written in English by Qi Z., Liu K., Zhao J. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Entity Set Expansion is an important task for open information extraction, which refers to expanding a given partial seed set to a more complete set that belongs to the same semantic class. Many previous researches have proved that the quality of seeds can influence expansion performance a lot since human-input seeds may be ambiguous, sparse etc. In this paper, we propose a novel method which can generate new, high-quality seeds and replace original, poor-quality ones. In our method, we leverage Wikipedia as a semantic knowledge to measure semantic relatedness and ambiguity of each seed. Moreover, to avoid the sparseness of the seed, we use web resources to measure its population. Then new seeds are generated to replace original, poor-quality seeds. Experimental results show that new seed sets generated by our method can improve entity expansion performance by up to average 9.1% over original seed sets.
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