Keywords: Relation Recognition Task, Dependency Grammar-based Relation Recognition Classifier
Notes
- Summarizes two approches:
- A surface pattern approach
- A [[Dependency_Grammar-based_Relation_Recognication_Classifier?]] approach
Experiments
Quotes
1. Introduction
- "In this chapter, we present two recent approaches to relation extraction that differ in terms of the kind of linguistic information they use:"
- "1. In the first method (Section 2), each potential relation is represented implicitly as a vector of features, where each feature corresponds to a word sequence anchored at the two entities forming the relationship. A relation extraction system is trained based on the subsequence kernel from [2]. This kernel is further generalized so that words can be replaced with word classes, thus enabling the use of information coming from POS tagging, named entity recognition, chunking or Wordnet [3]."
- "2. In the second approach (Section 3), the representation is centered on the shortest dependency path between the two entities in the dependency path between the two entities in the dependency graph of the sentence. Because syntactic analysis is essential in this method, its applicability is limited to domains where syntactic parsing gives reasonable accuracy."
3. A Dependency-Path Kernel for Relation Extraction
- Word-word dependencies are typically categorized in two classes as follows:
- [Local Dependencies] These correspond to local predicate-argument (or [[Head-Modifier?]]) constructions such as 'troops => raided', or 'pumping => stations' in Figure 4.
- [Non-local Dependencies] Long-distance dependencies arise due to various linguistic constructions such as Coordination, [[Extraction?]], [[Raising?]], and [[Control?]]. In Figure 4, among non-local dependencies are 'troops => warning', or 'ministers => preaching'.
- "A Context Free Grammar (CFG) parser can be used to extract local dependencies, which for each sentence from a dependency tree."
References
- [Bunescu and Mooney, 2005] => R. Bunescu and R. J. Mooney. 2005. A Shortest Path Dependency Kernel for Relation Extraction. In Proc. of HLT/EMNLP-2005. (paper.pdf)
- [LodhiSSCW, 2002] => H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins. (2002).
BibTex