Noun Compound Bracketing Algorithm

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A Noun Compound Bracketing Algorithm a Syntactic Parsing Algorithm that can solve a Compound Noun Bracketing Task.



1. Left-bracketing: [[noon fashion] show]
2. Right-bracketing: [noon [fashion show]]
In this example, the right-bracketing interpretation (a fashion show happening at noon) is more likely than the left-bracketing one (a show of noon fashion). However, the correct bracketing need not always be as obvious, some compounds can be subtler to bracket, e.g. car radio equipment (Girju et al., 2005).







  • (Lapata & Keller, 2005) ⇒ Mirella Lapata, and Frank Keller. (2005). “Web-based Models for Natural Language Processing.” In: ACM Transactions on Speech and Language Processing (TSLP), 2(1).
    • The first analysis task we consider is the syntactic disambiguation of compound nouns, which has received a fair amount of attention in the NLP literature [Pustejovsky et al. 1993; Resnik 1993; Lauer 1995].
    • Previous approaches typically compare diff erent bracketings and choose the most likely one. The adjacency model compares [n1 n2] against [n2 n3] and adopts a right branching analysis if [n2 n3] is more likely than [n1 n2]. The dependency model compares [n1 n2] against [n1 n3] and adopts a right branching analysis if [n1 n3] is more likely than [n1 n2].
    • The simplest model of compound noun disambiguation compares the frequencies of the two competing analyses and opts for the most frequent one [Pustejovsky et al. 1993]. Lauer [1995] proposes an unsupervised method for estimating the frequencies of the competing bracketings based on a taxonomy or a thesaurus. He uses a probability ratio to compare the probability of the left-branching analysis to that of the right-branching


  • (Lauer, 1995) ⇒ Mark Lauer. (1995). “Corpus Statistics Meet the Noun Compound: Some empirical results.” In: Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics.
    • ABSTRACT: A variety of statistical methods for noun compound analysis are implemented and compared. The results support two main conclusions. First, the use of conceptual association not only enables a broad coverage, but also improves the accuracy. Second, an analysis model based on dependency grammar is substantially more accurate than one based on deepest constituents, even though the latter is more prevalent in the literature.