What are the Productive Units of Natural Language Grammar? A DOP Approach to the Automatic Identification of Constructions.

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 29–36, New York City, June 2006. c⃝2006 Association for Computational Linguistics What are the Productive Units of Natural Language Grammar? A DOP Approach to the Automatic Identification of Constructions. Willem Zuidema Institute for Logic, Language and Computation University of Amsterdam Plantage Muidergracht 24, 1018 TV, Amsterdam, the Netherlands. jzuidema@science.uva.nl Abstract We explore a novel computational ap- proach to identifying “constructions” or “multi-word expressions” (MWEs) in an annotated corpus. In this approach, MWEs have no special status, but emerge in a general procedure for finding the best statistical grammar to describe the train- ing corpus. The statistical grammar for- malism used is that of stochastic tree sub- stitution grammars (STSGs), such as used in Data-Oriented Parsing. We present an algorithm for calculating the expected fre- quencies of arbitrary subtrees given the parameters of an STSG, and a method for estimating the parameters of an STSG given observed frequencies in a tree bank. We report quantitative results on the ATIS corpus of phrase-structure annotated sen- tences, and give examples of the MWEs extracted from this corpus. 1 Introduction Many current theories of language use and acquisi- tion assume that language users store and use much larger fragments of language than the single words and rules of combination of traditional linguistic models. Such fragments are often called construc- tions, and the theories that assign them a central role “construction grammar” (Goldberg, 1995; Kay and Fillmore, 1999; Tomasello, 2000; Jackendoff, 2002, among others). For construction grammar- ians, multi-word expressions (MWEs) such as id- ioms, collocations, fixed expressions and compound verbs and nouns, are not so much exceptions to the rule, but rather extreme cases that reveal some fun- damental properties of natural language. In the construction grammar tradition, co- occurrence statistics from corpora have often been used as evidence for hypothesized constructions. However, such statistics are typically gathered on a case-by-case basis, and no reliable procedure ex- ists to automatically identify constructions. In con- trast, in computational linguistics, many automatic procedures are studied for identifying MWEs (Sag et al., 2002) – with varying success – but here they are treated as exceptions: identifying multi-word ex- pressions is a pre-processing step, where typically adjacent words are grouped together after which the usual procedures for syntactic or semantic analysis can be applied. In this paper I explore an alter- native formal and computational approach, where multi-word constructions have no special status, but emerge in a general procedure to find the best statistical grammar to describe a training corpus. Crucially, I use a formalism known as “Stochastic Tree Substitution Grammars” (henceforth, STSGs), which can represent single words, contiguous and noncontiguous MWEs, context-free rules or com- plete parse trees in a unified representation. My approach is closely related to work in statisti- cal parsing known as Data-Oriented Parsing (DOP), an empirically highly successful approach with la- beled recall and precision scores on the Penn Tree Bank that are among the best currently obtained (Bod, 2003). DOP, …

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