This paper summarizes our participation to the CLPsych 2019 shared task, under the name CLaC. The goal of the shared task was to detect and assess suicide risk based on a collection of online posts. For our participation, we used an ensemble method which utilizes 8 neural sub-models to extract neural features and predict class probabilities, which are then used by an SVM classifier. Our team ranked first in 2 out of the 3 tasks (tasks A and C).
Native language identification (NLI) is the task of determining an author's native language, based on a piece of his/her writing in a second language. In recent years, NLI has received much attention due to its challenging nature and its applications in language pedagogy and forensic linguistics. We participated in the NLI Shared Task 2017 under the name UT-DSP. In our effort to implement a method for native language identification, we made use of a mixture of character and word Ngrams, and achieved an optimal F1-score of 0.7748, using both essay and speech transcription datasets.
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