Relation Mention Recognition Algorithm

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A relation mention recognition algorithm is a recognition algorithm (detection and classification) that can solve a relation mention recognition task.



References

2012

2009

2007a

2007b

2007c

2007d

2007 e.

2007f

2007g

  • (Fundel et al., 2007) ⇒ Katrin Fundel, R. Kuffner, and R. Zimmer. (2007). “RelEx--relation extraction using dependency parse trees." Bioinformatics. 2007 Feb 1;23(3):365-71.
    • QUOTE:The simplest approach is the detection of co-occurrences of entities from within sentences or abstracts (Ding et al., 2002; Jelier et al., 2005; Jenssen et al., 2001). It relies on the hypothesis that entities which are repeatedly mentioned together are somehow related. Extracted relations exhibit high sensitivity but very low specificity. Generally, the type and direction of the relation cannot be determined.

      Pattern based extraction approaches (Blaschke et al., 1999; Blaschke and Valencia, 2001; Leroy and Chen, 2002; Ono et al., 2001) were set up to increase specificity, yet they achieve significantly lower recall.

      As an extension to standard relation extraction pipelines, we propose the use of dependency parse trees (Klein and Manning, 2002, 2003; Mel’cuk, 1988) as a means for biomedical relation extraction. Dependency parse trees reveal non-local dependencies within sentences, i.e. between words that are far apart in a sentence. Sentences of biomedical texts tend to be long and complicated and frequently mention a number of possible effectors and effectees. Dependency parse trees provide a useful structure for the sentences by annotating edges with dependency types, e.g. subject, auxiliary, modifier.

2007h

2006a

2006b

2006c

2006d

2006 e.

2006f

2006g

2006h

2006i

  • (Greenwood and Stevenson, 2006) ⇒ M. A. Greenwood and M. Stevenson. (2006). “Improving Semi-Supervised Acquisition of Relation Extraction Patterns.” In: Proceedings of the Information Extraction Beyond The Document Workshop (COLING/ACL 2006). (paper.pdf)

2006

  • (Chakavarthy et al., 2006) ⇒ Venkatesan T. Chakaravarthy, H. Gupta, P. Roy, and M. Mohania. (2006). “Efficiently Linking Text Documents with Relevant Structured Information.” In: Proceedings of VLDB, 2006.

2005a

  • (Gonzalez et al., 2005) ⇒ M. Gonzalez, V. L. S. de Lima and J. V. de Lima. (2005). “Binary Lexical Relations for Text Representation in Information Retrieval.” In: Proceedings of 10th International Conference on Applications of Natural Language to Information Systems (NLDB-2005). (website)

2005b

2005c

2005d

  • (Moreda et al., 2005) ⇒ P. Moreda, B. Navarro and M. Palomar. (2005). “Using Semantic Roles in Information Retrieval Systems.” In: Proceedings of 10th International Conference on Applications of Natural Language to Information Systems (NLDB-2005). (website)

2005 e.

2005f

2005g

2005h

2005i

  • (Dong et al., 2005) ⇒ X. Dong, A. Halevy, and J. Madhavan. (2005). “Reference Reconciliation in Complex Information Spaces.” In: Proceedings of SIGMOD, 2005.

2004a

  • (Rosenfeld et al., 2004) ⇒ B. Rosenfeld, Ronen Feldman, M. Fresko, J. Schler, and Y. Auman. (2004). “TEG - A Hybrid Approach to Information Extraction.” In: Proceedings of the 2004 CIKM Conference (CIKM 2004).

2004b

2004c

2003

2001

2000a

2000b

  • (McCallum et al., 2000) ⇒ Andrew McCallum, K. Nigam, J. Rennie, and K. Seymore. (2000). “Automating the construction of internet portals with machine learning.” In: Information Retrieval Journal.

2000c

1999

1998a

1998b

  • (Giles et al., 1998) ⇒ C. L. Giles, K. Bollacker, and S. Lawrence. (1998). CiteSeer: An automatic citation indexing system. The Third ACM Conference on Digital Libraries.

1998c

1998d

1997a

  • (Khoo, 1997) ⇒ C. Khoo. 1997. The Use of Relation Matching in Information Retrieval. LIBRES: Library and Information Science Research Electronic Journal, 7(2). (paper.html)

1997b

1997c

1997d

  • (Soderland, 1997) ⇒ Stephen Soderland. (1997). “Learning to extract Text Based information from the World Wide Web.” In: ...?

1996

1995a

1995b

1993

  • (Riloff, 1993) ⇒ Ellen Riloff. (1993). “Automatically Constructing a Dictionary for Information Extraction Tasks.” In: Proceedings of the 11th Ann. Conference of Artificial Intelligence (AAAI 1993).

1992

  • (Hearst, 1992) ⇒ Marti Hearst. (1992). “Automatic Acquisition of Hyponyms from Large Text Corpora.” In: Proceedings of the 14th International Conference on Computational Linguistics (COLING-1992).(paper.pdf)

1991

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

1982

  • De Jong (1982)
    • NOTE: FRUMP system filled in Schank-style “scripts” from newswires; DARPA’s Message Understanding Conference (MUC) [87’-95’], and TIPSTER [92’-96’]

Notes

  • Early start was on news feeds and programed rules (e.g. FSM)
    • E.g. De Jong’s FRUMP [1982] that filled in Schank-style “scripts” from newswires; DARPA’s Message Understanding Conference (MUC) [87’-95’], and TIPSTER [92’-96’]
    • E.g. The finite state machines of SRI’s FASTUS
    • HMM’s in Elkan [Leek 1997]
    • BBN in [Bikel, et al, 1998]