Rule-Based Information Extraction is Dead! Long Live Rule-Based Information Extraction Systems!

By Laura Chiticariu, Yunyao Li, Frederick Reiss. Conference on Empirical Methods in Natural Language Processing, 2013

307 citations recorded by the source corpus.

The rise of “Big Data” analytics over unstructured text has led to renewed interest in information extraction (IE). We surveyed the landscape of IE technologies and identified a major disconnect between industry and academia: while rule-based IE dominates the commercial world, it is widely regarded as dead-end technology by the academia. We believe the disconnect stems from the way in which the two communities measure the benefits and costs of IE, as well as academia’s perception that rulebased IE is devoid of research challenges. We make a case for the importance of rule-based IE to industry practitioners. We then lay out a research agenda in advancing the state-of-theart in rule-based IE systems which we believe has the potential to bridge the gap between academic research and industry practice.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0692b9ad39145f57237199f3d4488667d5d9e5e7

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Citations in the corpus

Injecting Logical Background Knowledge into Embeddings for Relation Extraction (2015, background): “In fact, domain knowledge encoded as simple logic formulae over patterns and relations has been used in practice to directly specify relation extractors (Reiss et al., 2008; Chiticariu et al., 2013; Akbik et al., 2014).”
Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data (2020, background): “Many self-supervised pretraining schemes for NLP produce vector representations of sentences in which distance in vector space closely tracks semantic similarity (see e.g. recent results on the semantic textual similarity benchmark; Agirre et al., 2016).”
Improved Pattern Learning for Bootstrapped Entity Extraction (2014, background): “Though entity extraction using machine learning is common in academic research, rule-based systems dominate in commercial use (Chiticariu et al., 2013), mainly because rules are effective, interpretable, and are easy to customize by non-experts to cope with errors.”
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review (2019, unknown): “Finally, focusing on the clinical domain may have introduced a bias with respect to the methods reviewed (rule-based vs machine learning), as rule-based methods are more prevalent in the clinical domain compared with other domains [122].”
Knowledge Graphs (2020, unknown): “Aside from learning-based frameworks, manuallycrafted rules [88, 292] are still sometimes used due to their more controllable and predictable behaviour [89].”