Relation Mention Recognition Task

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A relation mention recognition task is a relation recognition task that is a mention recognition task (requires the identification and classification of semantic relation mentions within a document set.



References

2008

  • (Sarawagi, 2008) ⇒ Sunita Sarawagi. (2008). “Information extraction.” In: FnT Databases, 1(3).
    • The problem of relationship extraction has been studied extensively on natural language text, including news articles [1], scientific publications [166], Blogs, emails [113], and sources like Wikipedia [196, 197] and the general web [4, 14].

2007

2006

2005

  • (Bizer et al., 2005) ⇒ Christian Bizer, Ralf Heese, Malgorzata Mochol, Radoslaw Oldakowski, Robert Tolksdorf, and Rainer Eckstein. (2005). “The Impact of Semantic Web Technologies on Job Recruitment Processes.” 7. Internationale Tagung Wirtschaftsinformatik (WI 2005).

2004

2003

2002

  • (Roth and Yih, 2002) ⇒ Dan Roth and W. Yih. (2002). “Probabilistic Reasoning for Entity & Relation Recognition.” In: the 20th International Conference on Computational Linguistics (COLING-2002). paper.pdf
  • (Laender et al., 2002) ⇒ Alberto H. F. Laender, Berthier A. Ribeiro-Neto, Altigran S. da Silva, and Juliana S. Teixeira. (2002). “A Brief Survey of Web Data Extraction Tools.” In: ACM SIGMOD Record, 31(2). doi:10.1145/565117.565137

2001

  • Fabio Ciravegna. (2001). Adaptive information extraction from text by rule induction and generalization. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI 2001).
  • (Park, 2001) ⇒ J. C. Park. (2001). Using Combinatory Categorical Grammar to Extract Biomedical Information. In: IEEE Intelligent Systems.
    • applies parsing for automatic database curation from biomedical research papers.

2000

1999

  • Colins & Singer. (1999). “Unsupervised models for named entity classification.”

1998

1997

1996

1995

1993

  • Ellen Riloff. (1993). “Automatically constructing a dictionary for information extraction tasks."

1992

1991

  • L. Rau. (1991). “Extracting Company Names From Text.” In: Proceedings of the Sixth Conference on Artificial Intelligence Applications.


Notes

IE Task Open Issues

  • Integration of IE and TM [2003_ANoteOnUnifying...]
  • Allow for patterns to refer to generalized words. E.g. "to recognize as" <=> "to know as" by WordNet relationship (validate this example)
  • Weak theoretical underpinnings
  • The extraction of Long-Distance Dependency (LDD) and the mapping to shallow semantic representations is not always possible from the output of Syntactic Parsers.

Relation Types

  • Generic/Specific
    • Generic: InstanceOf(entity, class), IsA(subclass, class), PartOf(part, thing),
    • Specific: Cities(x), Elements(x), HeadquarterLocation(organization, location), DateOfBirth(person, date), Person(x)

IE Task Models, Summary

IE Task Evaluation Metrics

  • An IE system is typically evaluated in terms of:
  • Precision:
    • # of correct answers biven by the system / total # of answers given
  • Recall:
    • # of correct answers given by the system / total # of possible correct answers in the text
      • absolute
      • relative
  • Fallout:
    • # of incorrect answers given by the system / # of spurious facts in the text
  • F-measure: ...