2006 CombiningLinguisticExtractRelations

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Subject Headings: Relation Recognition from Text Algorithm, Dependency Grammar-based Relation Recognition Algorithm, LEILA System

Notes

Contributions

  1. Applies Syntactic Parsing information to Supervised Relation Recognition with high F-score.
  2. First known application of kNN and SVM to Pattern selection. Past approaches use Heuristics or [[.
  3. Proposes a Text Graph representation that is expressive and robust.
  4. The patterns reported in the empirical studies are interesting and reasonable. (see Comments)
  5. Achieves good empirical results on several datasets.
  6. Their system is available to test.

Input/Output

Input

  • positive examples
  • (relationship direction? one-to-one, one-to-many, many-to-one?)
  • annotated data (POS, Link, NER(e.g. person,date,...))

= Output

  • relation instances (& duplicates)
  • and their associated patterns

Patterns

Questions, Patterns

  • How are the boundaries of multi-word entities detected?

Algorithm

= Discovery Phase (Train Phase)

  • patterns are constructed from all sentences with both entities.

= Assessment Phase (Train Phase)

  • negative examples (counterexamples) are gathered
  • negative patterns are constructed
  • discriminative model induced

= Harvesting Phase (Test Phase)

= Questions, Algorithm

  • How does the counterexample selection know which entity is required to be fixed. E.g. in "Chopin"/"1957", would the algorithm know that the relationship is one-to-many and that the 'many' is associated with the year?

General Questions

  • What is the relationship of their approach with [Bunescu and Mooney, 2005] and with [Chiang and Yu, 2005]?
    • E.g. what is the difference in their definition of "shortest-path"?
  • It would be interesting to see all competitors evaluated.
  • The patterns reported in the empirical studies do not demonstrate the benefit of shortest path or of replacement nouns and adjectives. For example, the pattern used in figure 3 “<X> was <ADJECTIVE> among <Y>", is not one of the patterns reported in 4.2.1.
  • Why the discrepancy between the Snowball F-score performance of ~30% they report and the 80%+ figures reported in [Agichtein and Gravano, 2000]. This especially disconcerting given that the main pattern reported is the same one that Snowball (and DIPRE) have been shown to discover: "Y-based X".
  • The statements around the contribution of anaphora resolution are unconvincing. Clearly Snowball would also benefit directly from accurate tagging of anaphoras as entities.
  • There is no support for text that occurs before or after the entities, as in Snowball. This is a minor issue given that the value of prefiller and postfiller patterns.
  • The size of the test sets are very small; in the hundreds.
  • The size of the train sets is not reported

Cited By

Quotes

Abstract

  • The World Wide Web provides a nearly endless source of knowledge, which is mostly given in natural language. A first step towards exploiting this data automatically could be to extract pairs of a given semantic relation from text documents - for example all pairs of a person and her birthdate. One strategy for this task is to find text patterns that express the semantic relation, to generalize these patterns, and to apply them to a corpus to find new pairs. In this paper, we show that this approach profits significantly when deep linguistic structures are used instead of surface text patterns. We demonstrate how linguistic structures can be represented for machine learning, and we provide a theoretical analysis of the pattern matching approach. We show the benefits of our approach by extensive experiments with our prototype system LEILA.

References

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BibTeX

@InProceedings{conf/kdd/SuchanekIW06, 

 title =	"Combining linguistic and statistical analysis to

extract relations from web documents", 

 author =	"Fabian M. Suchanek and Georgiana Ifrim and Gerhard

Weikum", 

 bibdate =	"2006-10-05",
 bibsource =	"DBLP,

http://dblp.uni-trier.de/db/conf/kdd/kdd2006.html#SuchanekIW06", 

 booktitle =	"KDD",
 booktitle =	"Proceedings of the Twelfth {ACM} {SIGKDD}

International Conference on Knowledge Discovery and Data Mining, Philadelphia, {PA}, {USA}, August 20-23, 2006", 

 publisher =	"ACM",
 year = 	"2006",
 editor =	"Tina Eliassi-Rad and Lyle H. Ungar and Mark Craven and

Dimitrios Gunopulos", 

 ISBN = 	"1-59593-339-5",
 pages =	"712--717",
 URL =  	"http://dx.doi.org/10.1145/1150402.1150492",

}

,

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2006 CombiningLinguisticExtractRelationsFabian M. Suchanek
Gerhard Weikum
Georgiana Ifrim		Combining Linguistic and Statistical Analysis to Extract Relations from Web DocumentsProceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mininghttp://www.mpi-inf.mpg.de/~ifrim/publications/kdd2006.pdf10.1145/1150402.11504922006
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