Exploring the distribution of animacy : experiments on Norwegian

Animacy is a an inherent property of the referents of nouns wh ich as been claimed to figure as an influencing factor in a range of different grammatical pheno mena in various languages. In recent years several linguistic studies have examined the influence of ar gument 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 l anguages are sensitive to the dimension of animacy in the expression and interpretation of core syntac tic rguments (Lee, 2002; Øvrelid, 2004), either on a categorical level or as a strong statistical tend ency. This talk will report on machine learning experiments aimed at automatically acquiring animacy info rmation for common nouns in Norwegian (Øvrelid, 2006), which show that the animacy of a noun influen ces its linguistic distribution in such a consistent manner that an automatic classification based o n distributional features is worthwhile. By exploiting the strong correlation between the animacy di mension and other linguistic dimensions, new knowledge about the semantic and distributional proper ties 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 distri butional evidence from large corpora. Machine learning experiments are to a large degree dependen t on the set of features chosen to represent the data. A key generalisation or tendency observed in both t raditional 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); su bjects, for instance, will tend to be animate and agentive, whereas objects prototypically are inanimat e and themes/patients. Exceptions to this generalisation express a more markedstructure, a property which has consequences, for instance , for the distributional properties of the structure in quest ion. For these experiments, a set of seven morphosyntactic features were selected, features which in var ous ways approximate the multi-faceted property of animacy. The seven features are presented in Tab le 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 do ne on verb classification for intransitive verbs presented in Merlo and Stevenson (2001). For a set of fo rty highly frequent common nouns (20 animate, 20 inanimate), relative frequencies for the di ff rent morphosyntactic features described above were computed from the Oslo Corpus, a corpus of approxi mately 15 million words which has been automatically annotated with a Constraint Grammar tag ger1. 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 tha t are rather rare, and there is also quite a bit of variation in the data (represented by the standard dev iation for each class-feature combination).