Textual Information Extraction Task

Context:
- Input: Text Items.
- It can be solved by an Information Extraction from Text System (that implements an IE from text algorithm).
- It can require the ability to retrieve documents that are likely to contain the type of information sought. (See: Information Retrieval))
- It can require the ability to extract relations from documents. (See: Relation Extraction from Text Task.
- It can range from being a Heuristic Textual Information Extraction Task to being a Data-Driven Textual Information Extraction Task.
- It can be supported by an Information Retrieval Task.
- It can range from being an Open IE from Text Task to being a Closed IE from Text Task.
- ...
Example(s):
- Extraction of organizational mergers from news articles.
- Extraction of OrganizationHeadquarterLocation relation from news articles.
- Extraction of Protein Localization Relation Mentions, such as a PPLRE Task.
- Extraction of Argumentation Mentions.
- Extraction of research paper articles and authors from PDFs found on the Web. Requires ability to decide retrieve and identify research papers.
- a Knowledge-Base Population from Text Task.
- an Automated Dictionary Induction Task.
- a Keyphrase Extraction Task‎‎.
- …
Counter-Example(a):
- an IE from Semi-structured Data, such as IE from webpages.
- a Multimedia Information Extraction.
See: Text Mining, ACE, MUC.

References

(Staab, 2006) ⇒ Steffen Staab. (2006). “Ontologies and the Semantic Web." Tutorial at Semantic Mining in Bio-Medicine (SMBM-2006)

Günter Neumann & Feiyu Xu. (2004). Intelligent Information Extraction. Lecture Presentation
- http://www.dfki.de/~neumann/esslli04/reader/ie-lec1.pdf

(Appelt and Israel, 1999) ⇒ Douglas E. Appelt and D. J. Israel. (1999). “Introduction to Information Extraction Technology." A Tutorial Prepared for IJCAI-99.
(Cunningham, 1999) ⇒ H. Cunningham. (1999). “Information Extraction - A User Guide." Second Edition. Research memo CS-99-07, University of Sheffield, UK.
- It contains many MUC-like examples