Textual Information Extraction Task

An Textual Information Extraction Task is an information extraction task that is a text dataset analysis 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 IE Task to being a Data-Driven Textual IE Task.
  • It can range from being a Manual Textual IE Task to being an Automated Textual IE Task.
  • It can range from being an Open Textual IE Task to being a Closed Textual IE Task, depending on whether a data structure definition is provided.
  • 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.
  • Ontology-based Information Extraction (OBIE) Task.
  • …
• Counter-Example(a):
  • an IE from Semi-structured Data, such as IE from webpages.
  • a Multimedia Information Extraction.
• See: Text Mining, ACE, MUC.

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References

2024

• Perplexity
  • A textual information extraction task is a type of information extraction task that focuses specifically on extracting structured information from unstructured or semi-structured text data sources, such as documents, web pages, or other textual content.[1][2][3]
  • Examples of Textual Information Extraction Tasks
    1. **Named Entity Recognition (NER)**: Identifying and classifying named entities such as people, organizations, locations, dates, and numerical expressions from text.[1][3][4] For example, extracting "Barry Diller" as a person entity and "Vivendi Universal Entertainment" as an organization entity from the given sentence.[5]
    2. **Relation Extraction**: Identifying and classifying semantic relations between entities mentioned in the text.[1][3][4] For example, extracting the "part-whole" relation between the entities "Vivendi Universal Entertainment" and "Vivendi Universal" from the given sentence.[5]
    3. **Event Extraction**: Identifying and extracting event mentions, including the event type, event trigger words, and arguments (participants) involved in the event.[2][5] For example, extracting the "End-Position" event from the given sentence, with "quit" as the trigger word, "Barry Diller" as the person leaving the position, and "Vivendi Universal Entertainment" as the organization.[5]
  • Citations:

[1] https://www.researchgate.net/figure/An-example-of-an-information-extraction-system-extracting-the-relation_fig2_220225512
[2] https://aclanthology.org/M95-1026.pdf
[3] https://cs.nyu.edu/~grishman/IEtask15.book_2.html
[4] https://link.springer.com/chapter/10.1007/978-1-4614-3223-4_2
[5] https://link.springer.com/referenceworkentry/10.1007/978-0-387-39940-9_204

2006

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

• (Sekine, 2006)
  • On-demand: Queries

2004

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

1999

• (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

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