Supersense Tagging of Unknown Nouns in WordNet

Supersense Tagging of Unknown Nouns in WordNet � Massimiliano Ciaramita Brown University massi@brown.edu Mark Johnson Brown University mark johnson@brown.edu Abstract We present a new framework for classify- ing common nouns that extends named- entity classification. We used a fixed set of 26 semantic labels, which we called su- persenses. These are the labels used by lexicographers developing WordNet. This framework has a number of practical ad- vantages. We show how information con- tained in the dictionary can be used as ad- ditional training data that improves accu- racy in learning new nouns. We also de- fine a more realistic evaluation procedure than cross-validation. 1 Introduction Lexical semantic information is useful in many nat- ural language processing and information retrieval applications, particularly tasks that require com- plex inferences involving world knowledge, such as question answering or the identification of co- referential entities (Pasca and Harabagiu, 2001; Pustejovsky et al., 2002). However, even large lexical databases such as WordNet (Fellbaum, 1998) do not include all of the words encountered in broad-coverage NLP ap- plications. Ideally, we would like a system that automatically extends existing lexical resources by �We would like to thank Thomas Hofmann, Brian Roark, and our colleagues in the Brown Laboratory for Linguistic In- formation Processing (BLLIP), as well as Jesse Hochstadt for his editing advice. This material is based upon work supported by the National Science Foundation under Grant No. 0085940. identifying the syntactic and semantic properties of unknown words. In terms of the WordNet lexical database, one would like to automatically assign un- known words a position in the synset hierarchy, in- troducing new synsets and extending the synset hier- archy where appropriate. Doing this accurately is a difficult problem, and in this paper we address a sim- pler problem: automatically determining the broad semantic class, or supersense, to which unknown words belong. Systems for thesaurus extension (Hearst, 1992; Roark and Charniak, 1998), information extrac- tion (Riloff and Jones, 1999) or named-entity recog- nition (Collins and Singer, 1999) each partially ad- dress this problem in different ways. The goal in these tasks is automatically tagging words with semantic labels such as “vehicle”, “organization”, “person”, etc. In this paper we extend the named-entity recogni- tion approach to the classification of common nouns into 26 different supersenses. Rather than define these ourselves, we adopted the 26 “lexicographer class” labels used in WordNet, which include labels such as person, location, event, quantity, etc. We be- lieve our general approach should generalize to other definitions of supersenses. Using the WordNet lexicographer classes as su- persenses has a number of practical advantages. First, we show how information contained in the dic- tionary can be used as additional training data that improves the system’s accuracy. Secondly, it is pos- sible to use a very natural evaluation procedure. A system can be trained on an earlier release of Word- Net and tested on the words added in a later release, 1 person 7 cognition 13 attribute 19 quantity 25 plant 2 communication 8 possession 14 …

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