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The Deep Linguistic Processing with HPSG Initiative (DELH-IN) provides the infrastructure needed to produce open-source semantic transfer-based machine translation systems. We have made available a prototype Japanese-English machine translation system built from existing resources include parsers, generators, bidirectional grammars and a transfer engine.
Standard accounts of HPSG assume a distinction between morphology and syntax. However, despite decades of research, no cross-linguistically valid definition of 'word' has emerged (Haspelmath, 2010), suggesting that no sharp distinction is justified. Under such a view, the basic units are morphemes, rather than words, but it has been argued this raises problems when analysing phenomena such as zero inflection, syncretism, stem alternations, and extended exponence. We argue that with existing HPSG machinery, a morpheme-based approach can in fact deal with such issues. To illustrate this, we consider Slovene nominal declension and Georgian verb agreement, which have both been used to argue against constructive morpheme-based approaches. We overcome these concerns through use of a type hierarchy, and give a morpheme-based analysis which is simpler than the alternatives. Furthermore, we can recast notions from Word-and-Paradigm morphology, such as 'rule of referral' and 'stem space', in our framework. We conclude that using HPSG as a unified morphosyntactic theory is not only feasible, but also yields fruitful insights.
In this paper we investigate the phenomenon of verb-particle constructions, discussing their characteristics and the challenges that they present for a computational grammar. We concentrate our discussion on the treatment adopted in a wide-coverage HPSG grammar: the LinGO ERG. Given the constantly growing number of verb-particle combinations, possible ways of extending this treatment are investigated, taking into account the regular patterns found in some productive combinations of verbs and particles. We analyse possible ways of identifying regular patterns using different resources. One possible way to try to capture these is by means of lexical rules, and we discuss the difficulties encountered when adopting such an approach. We also investigate how to restrict the productivity of lexical rules to deal with subregularities and exceptions to the patterns found.