In this paper we present methods for automatically\u00a0acquiring training examples for the task\u00a0of entity extraction. Experimental evidence\u00a0show that: (1) our methods compete with a\u00a0current heavily supervised state-of-the-art system,\u00a0within 0.04 absolute mean average precision;\u00a0and (2) our model significantly outperforms\u00a0other supervised and unsupervised\u00a0baselines by between 0.15 and 0.30 in absolute\u00a0mean average precision.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper we present methods for automatically\u00a0acquiring training examples for the task\u00a0of entity extraction. Experimental evidence\u00a0show that: (1) our methods compete with a\u00a0current heavily supervised state-of-the-art system,\u00a0within 0.04 absolute mean average precision;\u00a0and (2) our model significantly outperforms\u00a0other supervised and unsupervised\u00a0baselines by between 0.15 and 0.30 in absolute\u00a0mean average precision.<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Computational Natural Language Learning (CONLL-11). 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