Thesis (M.A.(Research Psychology)) --University of Limpopo, 2016. / Refer to document

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Language ability can be a determining factor in deciding a defendant's legal guilt or innocence. This was the case for Chinese immigrant, LK, a licensed gun dealer who was criminally charged with selling weapons to ineligible buyers. In his defense, LK claimed that, as an English language learner, he did not possess the sufficient language skills required to adequately complete the complex administrative forms that accompany gun purchases. This study explores whether the defendant's English language abilities were sufficient to understand the government's requisite weapon sales report forms.Data collection comes from three sources: audio body bug (wiretap) tapes, written court transcripts, and the Alcohol, Tobacco, and Firearms' sales transaction forms. Using these sources as a basis for analysis, this study examines key linguistic features of the defendant's speech to evaluate his English language proficiency. Additionally, the language used by the defendant will be compared with the language found in the weapons purchase application report.Drawing upon theories on communicative competence (Bachman, 1990; Bachman & Palmer, 1996; Canale & Swain, 1980; Canale, 1983; Savignon, 1997), LK's functional, linguistic, discourse, sociolinguistic, and strategic competencies will first be evaluated. The second phase of the analysis uses the insight gained from examining LK's competencies in order to determine his level of proficiency. The Speaking Proficiency English Assessment Kit (1996, SPEAK®) test and the American Council on the Teaching of Foreign Languages (ACTFL) Proficiency Guidelines (2000) will be used. The third stage of the analysis turns to readability studies in order to determine the level of difficulty in the ATF form. Chall et al.'s (1996) rubric and Flesch's readability formula (1946, 1949, 1972, 1979) serve as the foundation for the readability assessment. And last, additional areas for a forensic linguistic investigation into this case are considered. These include thematic (topical) tracking, sociolinguistic practices, and speech accommodation.This research serves those interested in second language issues as well as members of the judicial system. It builds on and supports other work in linguistics, language acquisition, sociolinguistcs, as well as the more specialized field of forensic linguistics.

Verb classifications have attracted a great deal of interest in both linguistics and natural language processing (NLP). They have proved useful for important tasks and applications, including e.g. computational lexicography, parsing, word sense disambiguation, semantic role labelling, information extraction, question-answering, and machine translation (Swier and Stevenson, 2004; Dang, 2004; Shi and Mihalcea, 2005; Kipper et al., 2008; Zapirain et al., 2008; Rios et al., 2011). Particularly useful are classes which capture generalizations about a range of linguistic properties (e.g. lexical, (morpho-)syntactic, semantic), such as those proposed by Beth Levin (1993). However, full exploitation of such classes in real-world tasks has been limited because no comprehensive or domain-specific lexical classification is available. This thesis investigates how Levin-style lexical semantic classes could be learned automatically from corpus data. Automatic acquisition is cost-effective when it involves either no or minimal supervision and it can be applied to any domain of interest where adequate corpus data is available. We improve on earlier work on automatic verb clustering. We introduce new features and new clustering methods to improve the accuracy and coverage. We evaluate our methods and features on well-established cross-domain datasets in English, on a specific domain of English (the biomedical) and on another language (French), reporting promising results. Finally, our task-based evaluation demonstrates that the automatically acquired lexical classes enable new approaches to some NLP tasks (e.g. metaphor identification) and help to improve the accuracy of existing ones (e.g. argumentative zoning).