History
- 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
- More recently on the Web and Machine Learning
- Lehnert, Cardie, and Grishman
- HMM’s in Elkan [Leek 1997]
- BBN in [Bikel, et al, 1998]
- Tom Michel’s WebKB 1996
- Wrappers. Iniailly handcoded and then with induction. Soderland 1996, Kushmeric 1997.
References
2007
- [Bunescu and Mooney, 2007] => R. C. Bunescu and R. J. Mooney. (2007). Extracting Relations from Text: From Word Sequences to Dependency Paths. In, Text Mining and Natural Language Processing, Anne Kao and Steve Poteet (eds.), pp. 29-44, Springer.
- [Fundel et al, 2007] => K. Fundel, R. Kuffner, and R. Zimmer. (2007). RelEx--relation extraction using dependency parse trees. Bioinformatics. 2007 Feb 1;23(3):365-71.
- "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."
- [Jiang and Zhai, 2007] => J. Jiang and C. Zhai, (2007). A Systematic Exploration of the Feature Space for Relation Extraction. In Proc. of NAACL/HLT-2007.
2006
2005