Enhanced Named Entity Extraction via Error-Driven Aggregation

PDF Version Also Available for Download.

Description

Despite recent advances in named entity extraction technologies, state-of-the-art extraction tools achieve insufficient accuracy rates for practical use in many operational settings. However, they are not generally prone to the same types of error, suggesting that substantial improvements may be achieved via appropriate combinations of existing tools, provided their behavior can be accurately characterized and quantified. In this paper, we present an inference methodology for the aggregation of named entity extraction technologies that is founded upon a black-box analysis of their respective error processes. This method has been shown to produce statistically significant improvements in extraction relative to standard performance ... continued below

Physical Description

PDF-file: 9 pages; size: 1.2 Mbytes

Creation Information

Lemmond, T D; Perry, N C; Guensche, J W; Nitao, J J; Glaser, R E; Kidwell, P et al. February 22, 2010.

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Publisher

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

Description

Despite recent advances in named entity extraction technologies, state-of-the-art extraction tools achieve insufficient accuracy rates for practical use in many operational settings. However, they are not generally prone to the same types of error, suggesting that substantial improvements may be achieved via appropriate combinations of existing tools, provided their behavior can be accurately characterized and quantified. In this paper, we present an inference methodology for the aggregation of named entity extraction technologies that is founded upon a black-box analysis of their respective error processes. This method has been shown to produce statistically significant improvements in extraction relative to standard performance metrics and to mitigate the weak performance of entity extractors operating under suboptimal conditions. Moreover, this approach provides a framework for quantifying uncertainty and has demonstrated the ability to reconstruct the truth when majority voting fails.

Physical Description

PDF-file: 9 pages; size: 1.2 Mbytes

Source

  • Presented at: International Conference on Data Mining 2010, Las Vegas, NV, United States, Jul 12 - Jul 15, 2010

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

  • Report No.: LLNL-CONF-424662
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 1009212
  • Archival Resource Key: ark:/67531/metadc832181

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • February 22, 2010

Added to The UNT Digital Library

  • May 19, 2016, 3:16 p.m.

Description Last Updated

  • Dec. 5, 2016, 3:02 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 2

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Lemmond, T D; Perry, N C; Guensche, J W; Nitao, J J; Glaser, R E; Kidwell, P et al. Enhanced Named Entity Extraction via Error-Driven Aggregation, article, February 22, 2010; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc832181/: accessed October 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.