Supervised Information Extraction (IE) Task

A Supervised Information Extraction (IE) Task is a data-driven IE task that is a supervised learning task.

• AKA: Supervised IE.
• Context:
  • Input: an Annotated Corpus, or a Labeling Pattern.
  • It can be solved by a Supervised Information Extraction System (that implements a Supervised Information Extraction Algorithm.
  • It can range from being a Fully-Supervised Information Extraction Task to being a Semi-Supervised Information Extraction Task.
  • It can range from being a Supervised Information Extraction from Tables Task to being a Supervised Information Extraction from Text Task to being a Supervised Information Extraction from Images Task.
  • It can be supported by a Supervised Text Recognition Task.
  • …
• Example(s):
  • Supervised Person IE.
  • Supervised Semantic Relation Mention Extraction.
  • ...
• Counter-Example(s):
  • an Unsupervised Information Extraction Task.
• See: Supervised Information Retrieval Task, Supervised Semantic Annotation Task.

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References

2022

• (Jacobs & Hoste, 2022) ⇒ Gilles Jacobs, and Véronique Hoste. (2022). "SENTiVENT: Enabling supervised information extraction of company-specific events in economic and financial news.” In: Language Resources and Evaluation. Springer
  • NOTE: It introduces SENTiVENT, a corpus for company-specific events in English economic news, for event IE.

2022

• (Zhang et al., 2022) ⇒ Yue Zhang, Hongliang Fei, and Ping Li. (2022). "End-to-End Distantly Supervised Information Extraction with Retrieval Augmentation.” In: Proceedings of the 45th International ACM SIGIR Conference.
  • NOTE: It proposes a distantly-supervised IE algorithm with retrieval augmentation.

2021

• (Solawetz & Larson, 2021) ⇒ Jacob Solawetz, and Stefan Larson. (2021). "LSOIE: A large-scale dataset for supervised open information extraction.” arXiv preprint arXiv:2101.11177. [arxiv.org]
  • NOTE: It presents LSOIE, a large-scale dataset for supervised open information extraction, aimed at enhancing factual proposition extraction from sentences.

2008

• (Sarawagi, 2008) ⇒ Sunita Sarawagi. (2008). “Information extraction." FnT Databases, 1(3), 2008.
  • Information Extraction refers to the automatic extraction of structured information such as entities, relationships between entities, and attributes describing entities from unstructured sources. This enables much richer forms of queries on the abundant unstructured sources than possible with keyword searches alone. When structured and unstructured data co-exist, information extraction makes it possible to integrate the two types of sources and pose queries spanning them.
• (Buitelaar et al., 2008) ⇒ Paul Buitelaar, Philipp Cimiano, Anette Frank, Matthias Hartung, and Stefania Racioppa. (2008). “Ontology-based Information Extraction and Integration from Heterogeneous Data Sources.” In: International Journal of Human-Computer Studies, 66(11).

2007

• (Banko et al., 2007) ⇒ Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. (2007). “Open Information Extraction from the Web.” In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-2007).

2006

• (Paşca et al., 2006) ⇒ Marius Paşca, Dekang Lin, Jeffrey Bigham, Andrei Lifchits, and Alpa Jain. (2006). “Names and Similarities on the Web: fact extraction in the fast lane.” In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics

2005

• (McCallum, 2005) ⇒ Andrew McCallum. (2005). “Information Extraction: Distilling Structured Data from Unstructured Text.” In: ACM Queue, 3(9).
  • NOTES: It describes the Employment Posting Extraction Task.
• (Mooney & Bunescu, 2005) ⇒ Raymond Mooney, and Razvan C. Bunescu. (2005). “Mining knowledge from text using information extraction.” In: SIGKDD Explorations, 7(1).

2003

• (Grishman, 2003) ⇒ Ralph Grishman. (2003). “Information Extraction.” In: (Mitkov, 2003).

2001

• (Yangarber, 2001) ⇒ R. Yangarber. (2001). “Scenario Customization for Information Extraction." PhD Thesis, New York University.

1997

• (Grishman, 1997) ⇒ Ralph Grishman. (1997). “Information extraction: Techniques and challenges.” In: Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, International Summer School, (SCIE-97), pages 10–27, 1997.

1999

• (Soderland, 1999) ⇒ Stephen Soderland. (1999). “Learning Information Extraction Rules for Semi-Structured and Free Text.” In: Machine Learning, 44(1-3). doi:10.1023/A:1007562322031
• (Appelt and Israel, 1999) ⇒ Douglas E. Appelt and David Israel. (1999). “Introduction to Information Extraction Technology." Tutorial at IJCAI 1999.

1996

• (Strzalkowski & Wang, 1996) ⇒ Tomek Strzalkowski, and Jin Wang. (1996). “A Self-Learning Universal Concept Spotter.” In: Proceedings of 16th International Conference on Computational Linguistics (COLING 1996).

1995

• (Yarowsky, 1995) ⇒ David Yarowsky. (1995). “Unsupervised Word Sense Disambiguation Rivaling Supervised Methods.” In: Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL 1995). doi:10.3115/981658.981684

1993

• (Riloff, 2003) ⇒ Ellen Riloff (1993). “Automatically Constructing a Dictionary for Information Extraction Tasks.” In: Proceedings of AAAI-93.
• (Cardie, 1993) ⇒ Claire Cardie. (1993). “A Case-based Approach to Knowledge Acquisition for Domain-Specific Sentence Analysis.” In: Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93).

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