In natural languages, words can occur in single units called simplex words or in a group of simplex words that function as a single unit, called multiword expressions (MWEs). Although MWEs are similar to simplex words in their syntax and semantics, they pose their own sets of challenges (Sag et al. 2002). MWEs are arguably one of the biggest roadblocks in computational linguistics due to the bewildering range of syntactic, semantic, pragmatic and statistical idiomaticity they are associated with, and their high productivity. In addition, the large numbers in which they occur demand specialized handling. Moreover, dealing with MWEs has a broad range of applications, from syntactic disambiguation to semantic analysis in natural language processing (NLP) (Wacholder and Song 2003; Piao et al. 2003; Baldwin et al. 2004; Venkatapathy and Joshi 2006). / Our goals in this research are: to use computational techniques to shed light on the underlying linguistic processes giving rise to MWEs across constructions and languages; to generalize existing techniques by abstracting away from individual MWE types; and finally to exemplify the utility of MWE interpretation within general NLP tasks. / In this thesis, we target English MWEs due to resource availability. In particular, we focus on noun compounds (NCs) and verb-particle constructions (VPCs) due to their high productivity and frequency. / Challenges in processing noun compounds are: (1) interpreting the semantic relation (SR) that represents the underlying connection between the head noun and modifier(s); (2) resolving syntactic ambiguity in NCs comprising three or more terms; and (3) analyzing the impact of word sense on noun compound interpretation. Our basic approach to interpreting NCs relies on the semantic similarity of the NC components using firstly a nearest-neighbor method (Chapter 5), then verb semantics based on the observation that it is often an underlying verb that relates the nouns in NCs (Chapter 6), and finally semantic variation within NC sense collocations, in combination with bootstrapping (Chapter 7). / Challenges in dealing with verb-particle constructions are: (1) identifying VPCs in raw text data (Chapter 8); and (2) modeling the semantic compositionality of VPCs (Chapter 5). We place particular focus on identifying VPCs in context, and measuring the compositionality of unseen VPCs in order to predict their meaning. Our primary approach to the identification task is to adapt localized context information derived from linguistic features of VPCs to distinguish between VPCs and simple verb-PP combinations. To measure the compositionality of VPCs, we use semantic similarity among VPCs by testing the semantic contribution of each component. / Finally, we conclude the thesis with a chapter-by-chapter summary and outline of the findings of our work, suggestions of potential NLP applications, and a presentation of further research directions (Chapter 9).

This report describes computer studies which were done to determine the effect on burnup of modifying the heavy water reflector in a 600 MWe CANDU-PHW reactor. It is shown that the burnup penalty increases rapidly as the reflector thickness is reduced. The burnup penalty is significantly lower for mixed reflectors in which some of the heavy water in the outer region of the reflector is replaced by graphite, an organic liquid, or light water, while maintaining the original reflector thickness. / Thesis / Master of Engineering (MEngr)

This study applies major principles of cognitive linguistics to the task of developing a novel model of the bilingual dictionary called the dictionary for productive comprehension (DPC). Based on conceptual analysis and coherent network representation of entry words, multi-word expressions, and constructions, the DPC provides access to the conventional linguistic knowledge of native speakers. In seeing linguistic units as contentful symbolic forms, the DPC is designed with a view of language as a lexicogrammatical continuum. By constructing the bilingual dictionary at the intersections of the two languages’ concepts under clearly specified conditions of their neutralization, it is given theoretical status. However, the main purport of this study is in the realm of applied lexicography. Among its tasks are: operationalizing conceptual analysis by establishing heuristically viable discovery procedures; working out guidelines for converting conceptual networks into the microstructure of dictionary entries, and for organizing its macrostructure as a natural-language thesaurus of lexicalized and lexicogrammatical concepts; laying a foundation for selecting and locating MWEs, proverbial expressions and constructions in a principled way, and suggesting approaches to organizing the constructicon, the part of the dictionary that contains schematic constructions. The DPC model offers effective remedies for the two major faults of the conventional bilingual dictionary, i.e. unrecognizability of the SL entry as a coherent whole by the TL user, and, consequently, inability to suggest precise cognitive orientations for the user's own production of an equivalent TL text. It proves that the bilingual dictionary can be something other than an inventory of disparate senses, a boundless set of translation equivalents, or an eclectic mixture of the two. By maintaining conceptual integrity of linguistic units, DPC affords the user a means of grasping the essence of a foreign word, MWE, or construction as if they were units of one’s native speech, as well as a generative potential with regard to translating into the TL. At the same time, by making conventional linguistic knowledge of the native speaker explicit, DPC serves the purpose of a learner’s dictionary.