A persistent challenge in the creation of semantically classified dictionaries and lexical resources is the lengthy and expensive process of manual semantic classification, a hindrance which can make adequate semantic resources unattainable for under-resourced language communities. We explore here an alternative to manual classification using a vector semantic method, which, although not yet at the level of human sophistication, can provide usable first-pass semantic classifications in a fraction of the time. As a case example, we use a dictionary in Plains Cree (ISO: crk, Algonquian, Western Canada and United States)

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This paper discusses the development and evaluation of a Speech Synthesizer for Plains Cree, an Algonquian language of North America. Synthesis is achieved using Simple4All and evaluation was performed using a modified Cluster Identification, Semantically Unpredictable Sentence, and a basic dichotomized judgment task. Resulting synthesis was not well received; however, observations regarding the process of speech synthesis evaluation in North American indigenous communities were made: chiefly, that tolerance for variation is often much lower in these communities than for majority languages. The evaluator did not recognize grammatically consistent but semantically nonsense strings as licit language. As a result, monosyllabic clusters and semantically unpredictable sentences proved not the most appropriate evaluate tools. Alternative evaluation methods are discussed.

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The composition of richly-inflected words in morphologically complex languages can be a challenge for language learners developing literacy. Accordingly, Lane and Bird (2020) proposed a finite state approach which maps prefixes in a language to a set of possible completions up to the next morpheme boundary, for the incremental building of complex words. In this work, we develop an approach to morph-based auto-completion based on a finite state morphological analyzer of Plains Cree (nêhiyawêwin), showing the portability of the concept to a much larger, more complete morphological transducer. Additionally, we propose and compare various novel ranking strategies on the morph auto-complete output. The best weighting scheme ranks the target completion in the top 10 results in 64.9% of queries, and in the top 50 in 73.9% of queries.