In the context of the ongoing AGGREGATION project concerned with inferring grammars from interlinear glossed text, we explore the integration of morphological patterns extracted from IGT data with inferred syntactic properties in the context of creating implemented linguistic grammars. We present a case study of Chintang, in which we put emphasis on evaluating the accuracy of these predictions by using them to generate a grammar and parse running text. Our coverage over the corpus is low because the lexicon produced by our system only includes intransitive and transitive verbs and nouns, but it outperforms an expert-built, oracle grammar of similar scope.
Zamaraeva, Olga; Howell, Kristen; and Bender, Emily M.
"Handling Cross-cutting Properties in Automatic Inference of Lexical Classes: A Case Study of Chintang,"
Proceedings of the Workshop on Computational Methods for Endangered Languages: Vol. 1
, Article 5.
Available at: https://scholar.colorado.edu/scil-cmel/vol1/iss1/5