Learning the Countability of English Nouns from Corpus Data

Learning the Countability of English Nouns from Corpus Data Timothy Baldwin CSLI Stanford University Stanford, CA, 94305 tbaldwin@csli.stanford.edu Francis Bond NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation Kyoto, Japan bond@cslab.kecl.ntt.co.jp Abstract This paper describes a method for learn- ing the countability preferences of English nouns from raw text corpora. The method maps the corpus-attested lexico-syntactic properties of each noun onto a feature vector, and uses a suite of memory-based classifiers to predict membership in 4 countability classes. We were able to as- sign countability to English nouns with a precision of 94.6%. 1 Introduction This paper is concerned with the task of knowledge- rich lexical acquisition from unannotated corpora, focusing on the case of countability in English. Knowledge-rich lexical acquisition takes unstruc- tured text and extracts out linguistically-precise cat- egorisations of word and expression types. By combining this with a grammar, we can build broad-coverage deep-processing tools with a min- imum of human effort. This research is close in spirit to the work of Light (1996) on classi- fying the semantics of derivational affixes, and Siegel and McKeown (2000) on learning verb as- pect. In English, nouns heading noun phrases are typ- ically either countable or uncountable (also called count and mass). Countable nouns can be modi- fied by denumerators, prototypically numbers, and have a morphologically marked plural form: one dog, two dogs. Uncountable nouns cannot be modi- fied by denumerators, but can be modified by unspe- cific quantifiers such as much, and do not show any number distinction (prototypically being singular): *one equipment, some equipment, *two equipments. Many nouns can be used in countable or uncountable environments, with differences in interpretation. We call the lexical property that determines which uses a noun can have the noun’s countability prefer- ence. Knowledge of countability preferences is im- portant both for the analysis and generation of En- glish. In analysis, it helps to constrain the inter- pretations of parses. In generation, the countabil- ity preference determines whether a noun can be- come plural, and the range of possible determin- ers. Knowledge of countability is particularly im- portant in machine translation, because the closest translation equivalent may have different countabil- ity from the source noun. Many languages, such as Chinese and Japanese, do not mark countability, which means that the choice of countability will be largely the responsibility of the generation compo- nent (Bond, 2001). In addition, knowledge of count- ability obtained from examples of use is an impor- tant resource for dictionary construction. In this paper, we learn the countability prefer- ences of English nouns from unannotated corpora. We first annotate them automatically, and then train classifiers using a set of gold standard data, taken from COMLEX (Grishman et al., 1998) and the trans- fer dictionaries used by the machine translation sys- tem ALT-J/E (Ikehara et al., 1991). The classifiers and their training are described in more detail in Baldwin and Bond (2003). These are then run over the corpus to extract nouns as members of four classes — countable: …

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