Reliability of Automatic Linguistic Annotation: Native vs Non-native Texts
Elena Volodina, David Alfter, Therese Lindström Tiedemann, Maisa Lauriala, Daniela Piipponen · Linköping electronic conference proceedings · 2022
We present the results of a manual evaluation of the performance of automatic linguistic annotation on three different datasets: (1) texts written by native speakers, (2) essays written by second language (L2) learners of Swedish in the original form and (3) the normalized versions of learner-written essays. The focus of the evaluation is on lemmatization, POS-tagging, word sense disambiguation, multi-word detection and dependency annotation. Two annotators manually went through the automatic annotation on a subset of the datasets and marked up all deviations based on their expert judgments and the guidelines provided. We report Inter-Annotator Agreement between the two annotators and accuracy for the linguistic annotation quality for the three datasets, by levels and linguistic features.