Phonotactic probability in Amharic : : a psycholinguistic and computational investigation

In this dissertation, the robustness of the relationship between the lexical frequency of phonotactic patterns and word-acceptability is examined for words of Amharic, an understudied Semitic language. The patterns under investigation span the whole verb root and include both under-represented and over-represented consonant distributions in the lexicon. A state-of-the-art probabilistic model, the Maximum Entropy phonotactic learner, is used to acquire a phonotactic grammar from the input (the lexicon) and the predictions of that grammar are compared with the results of two Amharic nonce-word rating tasks designed specifically to investigate a range of consonantal phonotactic patterns. The first task investigates consonant co-occurrence patterns (homorganic consonants, identical consonants, and fricatives). In the Amharic verb lexicon, identical consonants are under- represented in some locations and over-represented in others whereas homorganic consonants and fricatives (a previously unknown pattern independently acquired by the model) are under-represented. The phonotactic learner successfully learned the under-represented patterns and the comparison between the model predictions and the experimental results show evidence for a relationship between lexical frequency and word acceptability for under -representation. However, speaker judgements show no preference for over-representation. The second task examines the distribution of single consonants within the verb root with respect to under-representation, over- representation and positional restrictions. Evidence for a relationship between lexical frequency and phonotactic probability was observed for both under-represented and over-represented consonants, but tied to a particular location. The correlation between speaker judgments and model predictions is low for this task, due in part to the way the model deals with over-representation. This investigation demonstrates not only that word acceptability is influenced by phonotactic probability for both under-represented and over-represented patterns, but also that probabilistic models can be used to investigate the phonotactics of a language, even in the absence of speaker judgement data. These models can therefore be used to assess the phonotactics of languages where experimental data is difficult to obtain and broaden our knowledge of phonotactic typolog

Modeling OCP-Place in Amharic with the Maximum Entropy phonotactic learner

One essential part of a native speaker’s knowledge is the characterization of what logically possible sound sequences constitute legitimate possible words in the speaker’s language, or phonotactics. Although formal phonotactic models were originally categorical—classifying every sound sequence dichotomously as eithe

Phonological naturalness and phonotactic learning *

We investigate whether the patterns of phonotactic well-formedness internalized by language learners are direct reflections of the phonological patterns they encounter, or reflect in addition principles of phonological naturalness. As a research tool we employ the phonotactic learning system of Hayes and Wilson (2008), which carries out an unbiased search of the lexicon for valid phonotactic generalizations. Applying this system to English data, we find that it learns many constraints that seem to be unnatural—they have no evident typological or phonetic basis, yet hold true of the English lexicon. We tested the status of ten of these constraints in a nonce-probe study, obtaining nativespeaker ratings of novel words that violated them. We used 40 such words: 10 violating our unnatural constraints, 10 violating natural constraints assigned comparable weights by the Hayes/Wilson learner, and 20 violation-free forms, each similar to a test form and employed as a control. In our experiment, we found that violations of the natural constraints had a powerful effect on native speaker judgment and violations of the unnatural constraints had at best a weak one. We conclude by assessing a variety of hypotheses intended to explain this disparity, opting ultimately for a learning bias account. Author emails