A Taxonomy, Dataset, and Classifier for Automatic Noun Compound Interpretation

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 678–687, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics A Taxonomy, Dataset, and Classifier for Automatic Noun Compound Interpretation Stephen Tratz and Eduard Hovy Information Sciences Institute University of Southern California Marina del Rey, CA 90292 {stratz,hovy}@isi.edu Abstract The automatic interpretation of noun-noun compounds is an important subproblem within many natural language processing applications and is an area of increasing interest. The problem is difficult, with dis- agreement regarding the number and na- ture of the relations, low inter-annotator agreement, and limited annotated data. In this paper, we present a novel taxonomy of relations that integrates previous rela- tions, the largest publicly-available anno- tated dataset, and a supervised classifica- tion method for automatic noun compound interpretation. 1 Introduction Noun compounds (e.g., ‘maple leaf’) occur very frequently in text, and their interpretation— determining the relationships between adjacent nouns as well as the hierarchical dependency structure of the NP in which they occur—is an important problem within a wide variety of nat- ural language processing (NLP) applications, in- cluding machine translation (Baldwin and Tanaka, 2004) and question answering (Ahn et al., 2005). The interpretation of noun compounds is a difficult problem for various reasons (Spärck Jones, 1983). Among them is the fact that no set of relations pro- posed to date has been accepted as complete and appropriate for general-purpose text. Regardless, automatic noun compound interpretation is the fo- cus of an upcoming SEMEVAL task (Butnariu et al., 2009). Leaving aside the problem of determining the dependency structure among strings of three or more nouns—a problem we do not address in this paper—automatic noun compound interpretation requires a taxonomy of noun-noun relations, an automatic method for accurately assigning the re- lations to noun compounds, and, in the case of su- pervised classification, a sufficiently large dataset for training. Earlier work has often suffered from using tax- onomies with coarse-grained, highly ambiguous predicates, such as prepositions, as various labels (Lauer, 1995) and/or unimpressive inter-annotator agreement among human judges (Kim and Bald- win, 2005). In addition, the datasets annotated ac- cording to these various schemes have often been too small to provide wide coverage of the noun compounds likely to occur in general text. In this paper, we present a large, fine-grained taxonomy of 43 noun compound relations, a dataset annotated according to this taxonomy, and a supervised, automatic classification method for determining the relation between the head and modifier words in a noun compound. We com- pare and map our relations to those in other tax- onomies and report the promising results of an inter-annotator agreement study as well as an au- tomatic classification experiment. We examine the various features used for classification and iden- tify one very useful, novel family of features. Our dataset is, to the best of our knowledge, the largest noun compound dataset yet produced. We will make it available via http://www.isi.edu. 2 Related Work 2.1 Taxonomies The relations between the component nouns in …

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