The main aim of this paper is to investigate how far applying suitably conceived and designed credit scoring models can properly account for the incidence of default and help improve the decision-making process. Four statistical modelling techniques, namely, discriminant analysis, logistic regression, multi-layer feed-forward neural network and probabilistic neural network are used in building credit scoring models for the Indian banking sector. Notably actual misclassification costs are analysed in preference to estimated misclassification costs. Our first-stage scoring models show that sophisticated credit scoring models, in particular probabilistic neural networks, can help to strengthen the decision-making processes by reducing default rates by over 14%. The second-stage of our analysis focuses upon the default cases and substantiates the significance of the timing of default. Moreover, our results reveal that State of residence, equated monthly instalment, net annual income, marital status and loan amount, are the most important predictive variables. The practical implications of this study are that our scoring models could help banks avoid high default rates, rising bad debts, shrinking cash flows and punitive cost-cutting measures.

This paper examines F0 correlates of morphologically marked grammatical functions, in particular topic and subject, in spontaneous Japanese speech. Our data consist of F0 measurements of 7,106 nouns in the CallHome Japanese corpus of telephone conversations [4]. We find that topics exhibit higher peak F0 than subjects, contradicting information-structure accounts which predict that topics, which refer to `old' information, should be less prominent. However, we suggest that the style and genre of speech is an important factor in this regard. 1. INTRODUCTION In Japanese, the grammatical function of a noun phrase (NP) in a sentence is indicated by a postpositional particle like -wa (topic), -ga (subject), -ni (indirect object), or -o (direct object). This is illustrated in the list of sentences in (1), based on utterance (1a) from our corpus of telephone conversations (cf. Figure 1). (1) (a) kareshi-ga boyfriend-SUBJ tsurete take kuru? come `Will her boyfriend bring her?' (b) ka...

This paper reports on our work on the spoken dialogue interface to Jijo-2, an autonomous mobile office robot. We propose an attentional state model for office robot dialogue based on the centering model [2]. We give details of the implementation of Jijo-2's language components and give our algorithm for reducing the ambiguity of dialogues by identifying pronoun referents.

Recent work on the syntax-semantics interface (see e.g. (Dalrymple et al., 1994)) uses a fragment of linear logic as a 'glue language' for assembling meanings compositionally. This paper presents a glue language account of how negative polarity items (e.g. ever, any) get licensed within the scope of negative or downward-entailing contexts (Ladusaw, 1979), e.g. Nobody ever left. This treatment of licensing operates precisely at the syntax-semantics interface, since it is carried out entirely within the interface glue language (linear logic). In addition to the account of negative polarity licensing, we show in detail how linear-logic proof nets (Girard, 1987; Gallier, 1992) can be used for efficient meaning deduction within this 'glue language' framework.