Exploring the distribution of animacy: experiments on Norwegian

Animacy is a an inherent property of the referents of nouns which has been claimed to figure as an influencing factor in a range of different grammatical phenomena in various languages. In recent years several linguistic studies have examined the influence of argument animacy in grammatical phenomena such as differential object marking (Aissen, 2003), the passive construction (Dingare, 2001), the dative alternation (Bresnan et al., 2005) etc. A variety of languages are sensitive to the dimension of animacy in the expression and interpretation of core syntactic arguments (Lee, 2002; Øvrelid, 2004), either on a categorical level or as a strong statistical tendency. This talk will report on machine learning experiments aimed at automatically acquiring animacy information for common nouns in Norwegian (Øvrelid, 2006), which show that the animacy of a noun influences its linguistic distribution in such a consistent manner that an automatic classification based on distributional features is worthwhile. By exploiting the strong correlation between the animacy dimension and other linguistic dimensions, new knowledge about the semantic and distributional properties of various constructions may be obtained with very little manual effort. The experiments also raise the question of how the dimension of animacy may be conceptualised and delimited based on distributional evidence from large corpora. Machine learning experiments are to a large degree dependent on the set of features chosen to represent the data. A key generalisation or tendency observed in both traditional typological linguistics, as well as the more recent linguistic studies where animacy figures, is that prominent grammatical features tend to attract other prominent features (Aissen, 2003); subjects, for instance, will tend to be animate and agentive, whereas objects prototypically are inanimate and themes/patients. Exceptions to this generalisation express a more marked structure, a property which has consequences, for instance, for the distributional properties of the structure in question. For these experiments, a set of seven morphosyntactic features were selected, features which in various ways approximate the multi-faceted property of animacy. The seven features are presented in Table 1. In particular, these features exploit the strong correlation that animacy has to other linguistic dimensions, such as agentivity and discourse salience. The experimental methodology is inspired by experiments done on verb classification for intransitive verbs presented in Merlo and Stevenson (2001). For a set of forty highly frequent common nouns (20 animate, 20 inanimate), relative frequencies for the different morphosyntactic features described above were computed from the Oslo Corpus, a corpus of approximately 15 million words which has been automatically annotated with a Constraint Grammar tagger1. The mean relative frequencies for each class animate and inanimate are presented in the first two rows of Table 2. As we can see, quite a few of the features express morphosyntactic cues that are rather rare, and there is also quite a bit of variation in the data (represented by the standard deviation for each class-feature combination).