Many techniques developed in Natural Language Processing (NLP), a field of research concerned with using computers to process human languages, have moved from research labs to consumer products in recent years. Some well-known products backed by these technologies include the grammar checker in Microsoft Word, Google Translate, and speech-driven digital assistants such as Apple’s Siri. Natural language processing is also being used by businesses in various ways, for example in fraud detection, legal investigations, and marketing. This thesis develops extant and novel techniques to apply NLP to the automation of language sample analysis. Language sample analysis (LSA, not to be confused with latent semantic analysis, which is not discussed in this thesis) is the practice of eliciting, transcribing, and analyzing samples of spoken language. At present, LSA is used for a variety of purposes, including research into language development and developmental disorders (e.g. autism and language impairments), and less commonly, for assessing a child’s language or evaluating the effectiveness of remediation efforts.