Unsupervised Methods for Head Assignments

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 701–709, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Unsupervised Methods for Head Assignments Federico Sangati, Willem Zuidema Institute for Logic, Language and Computation University of Amsterdam, the Netherlands {f.sangati,zuidema}@uva.nl Abstract We present several algorithms for assign- ing heads in phrase structure trees, based on different linguistic intuitions on the role of heads in natural language syntax. Start- ing point of our approach is the obser- vation that a head-annotated treebank de- fines a unique lexicalized tree substitution grammar. This allows us to go back and forth between the two representations, and define objective functions for the unsu- pervised learning of head assignments in terms of features of the implicit lexical- ized tree grammars. We evaluate algo- rithms based on the match with gold stan- dard head-annotations, and the compar- ative parsing accuracy of the lexicalized grammars they give rise to. On the first task, we approach the accuracy of hand- designed heuristics for English and inter- annotation-standard agreement for Ger- man. On the second task, the implied lex- icalized grammars score 4% points higher on parsing accuracy than lexicalized gram- mars derived by commonly used heuris- tics. 1 Introduction The head of a phrasal constituent is a central concept in most current grammatical theories and many syntax-based NLP techniques. The term is used to mark, for any nonterminal node in a syn- tactic tree, the specific daughter node that fulfills a special role; however, theories and applications differ widely in what that special role is supposed to be. In descriptive grammatical theories, the role of the head can range from the determinant of agreement or the locus of inflections, to the gover- nor that selects the morphological form of its sis- ter nodes or the constituent that is distributionally equivalent to its parent (Corbett et al., 2006). In computational linguistics, heads mainly serve to select the lexical content on which the probability of a production should depend (Char- niak, 1997; Collins, 1999). With the increased popularity of dependency parsing, head annota- tions have also become a crucial level of syntac- tic information for transforming constituency tree- banks to dependency structures (Nivre et al., 2007) or richer syntactic representations (e.g., Hocken- maier and Steedman, 2007). For the WSJ-section of the Penn Treebank, a set of heuristic rules for assigning heads has emerged from the work of (Magerman, 1995) and (Collins, 1999) that has been employed in a wide variety of studies and proven extremely useful, even in rather different applications from what the rules were originally intended for. However, the rules are specific to English and the treebank’s syntactic an- notation, and do not offer much insights into how headedness can be learned in principle or in prac- tice. Moreover, the rules are heuristic and might still leave room for improvement with respect to recovering linguistic head assignment even on the Penn WSJ corpus; in fact, we find that the head- assignments …

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