2015/06/21 by Raphael Hoffmann, Luke Zettlemoyer, Hoffmann, Raphael +3
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #H.2.8 #H.3.1 #I.2.7 #I.5.5 #Information Retrieval (cs.IR) #Natural Language Processing Techniques #Topic Modeling #cs.AI #cs.CL #cs.IR
paper · pdf · doi:10.48550/arxiv.1506.06418
arxiv created 2015/06/21 · openalex publication_date 2015/06/21 · arxiv updated 2015/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Information Extraction (IE) aims to automatically generate a large knowledge base from natural language text, but progress remains slow. Supervised learning requires copious human annotation, while unsupervised and weakly supervised approaches do not deliver competitive accuracy. As a result, most fielded applications of IE, as well as the leading TAC-KBP systems, rely on significant amounts of manual engineering. Even "Extreme" methods, such as those reported in Freedman et al. 2011, require about 10 hours of expert labor per relation. This paper shows how to reduce that effort by an order of magnitude. We present a novel system, InstaRead, that streamlines authoring with an ensemble of methods: 1) encoding extraction rules in an expressive and compositional representation, 2) guiding the user to promising rules based on corpus statistics and mined resources, and 3) introducing a new interactive development cycle that provides immediate feedback --- even on large datasets. Experiments show that experts can create quality extractors in under an hour and even NLP novices can author good extractors. These extractors equal or outperform ones obtained by comparably supervised and state-of-the-art distantly supervised approaches.