Fully Unsupervised Core-Adjunct Argument Classification

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 226–236, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Fully Unsupervised Core-Adjunct Argument Classification Omri Abend∗ Institute of Computer Science The Hebrew University omria01@cs.huji.ac.il Ari Rappoport Institute of Computer Science The Hebrew University arir@cs.huji.ac.il Abstract The core-adjunct argument distinction is a basic one in the theory of argument struc- ture. The task of distinguishing between the two has strong relations to various ba- sic NLP tasks such as syntactic parsing, semantic role labeling and subcategoriza- tion acquisition. This paper presents a novel unsupervised algorithm for the task that uses no supervised models, utilizing instead state-of-the-art syntactic induction algorithms. This is the first work to tackle this task in a fully unsupervised scenario. 1 Introduction The distinction between core arguments (hence- forth, cores) and adjuncts is included in most the- ories on argument structure (Dowty, 2000). The distinction can be viewed syntactically, as one between obligatory and optional arguments, or semantically, as one between arguments whose meanings are predicate dependent and indepen- dent. The latter (cores) are those whose function in the described event is to a large extent determined by the predicate, and are obligatory. Adjuncts are optional arguments which, like adverbs, modify the meaning of the described event in a predictable or predicate-independent manner. Consider the following examples: 1. The surgeon operated [on his colleague]. 2. Ron will drop by [after lunch]. 3. Yuri played football [in the park]. The marked argument is a core in 1 and an ad- junct in 2 and 3. Adjuncts form an independent semantic unit and their semantic role can often be inferred independently of the predicate (e.g., [af- ter lunch] is usually a temporal modifier). Core ∗ Omri Abend is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. roles are more predicate-specific, e.g., [on his col- league] has a different meaning with the verbs ‘op- erate’ and ‘count’. Sometimes the same argument plays a different role in different sentences. In (3), [in the park] places a well-defined situation (Yuri playing foot- ball) in a certain location. However, in “The troops are based [in the park]”, the same argument is obligatory, since being based requires a place to be based in. Distinguishing between the two argument types has been discussed extensively in various formu- lations in the NLP literature, notably in PP attach- ment, semantic role labeling (SRL) and subcatego- rization acquisition. However, no work has tack- led it yet in a fully unsupervised scenario. Unsu- pervised models reduce reliance on the costly and error prone manual multi-layer annotation (POS tagging, parsing, core-adjunct tagging) commonly used for this task. They also allow to examine the nature of the distinction and to what extent it is accounted for in real data in a theory-independent manner. In this paper we present a fully unsupervised al- gorithm for core-adjunct classification. We utilize leading fully unsupervised grammar induction and POS induction algorithms. We focus on preposi- tional arguments, …

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