Prototype-Driven Grammar Induction

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 881–888, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Prototype-Driven Grammar Induction Aria Haghighi Computer Science Division University of California Berkeley aria42@cs.berkeley.edu Dan Klein Computer Science Division University of California Berkeley klein@cs.berkeley.edu Abstract We investigate prototype-driven learning for pri- marily unsupervised grammar induction. Prior knowledge is specified declaratively, by providing a few canonical examples of each target phrase type. This sparse prototype information is then propa- gated across a corpus using distributional similar- ity features, which augment an otherwise standard PCFG model. We show that distributional features are effective at distinguishing bracket labels, but not determining bracket locations. To improve the qual- ity of the induced trees, we combine our PCFG in- duction with the CCM model of Klein and Manning (2002), which has complementary stengths: it iden- tifies brackets but does not label them. Using only a handful of prototypes, we show substantial im- provements over naive PCFG induction for English and Chinese grammar induction. 1 Introduction There has been a great deal of work on unsuper- vised grammar induction, with motivations rang- ing from scientific interest in language acquisi- tion to engineering interest in parser construc- tion (Carroll and Charniak, 1992; Clark, 2001). Recent work has successfully induced unlabeled grammatical structure, but has not successfully learned labeled tree structure (Klein and Manning, 2002; Klein and Manning, 2004; Smith and Eis- ner, 2004) . In this paper, our goal is to build a system capa- ble of producing labeled parses in a target gram- mar with as little total effort as possible. We in- vestigate a prototype-driven approach to grammar induction, in which one supplies canonical ex- amples of each target concept. For example, we might specify that we are interested in trees which use the symbol NP and then list several examples of prototypical NPs (determiner noun, pronouns, etc., see figure 1 for a sample prototype list). This prototype information is similar to specifying an annotation scheme, which even human annotators must be provided before they can begin the con- struction of a treebank. In principle, prototype- driven learning is just a kind of semi-supervised learning. However, in practice, the information we provide is on the order of dozens of total seed in- stances, instead of a handful of fully parsed trees, and is of a different nature. The prototype-driven approach has three strengths. First, since we provide a set of target symbols, we can evaluate induced trees using standard labeled parsing metrics, rather than the far more forgiving unlabeled metrics described in, for example, Klein and Manning (2004). Second, knowledge is declaratively specified in an inter- pretable way (see figure 1). If a user of the system is unhappy with its systematic behavior, they can alter it by altering the prototype information (see section 7.1 for examples). Third, and related to the first two, one does not confuse the ability of the system to learn a consistent grammar with its …

Content truncated to 500 words