In-tool Learning for Selective Manual Annotation in Large Corpora

We present a novel approach to the selective annotation of large corpora through the use of machine learning. Linguistic search engines used to locate potential instances of an infrequent phenomenon do not support ranking the search results. This favors the use of high-precision queries that return only a few results over broader queries that have a higher recall. Our approach introduces a classifier used to rank the search results and thus helping the annotator focus on those results with the highest potential of being an instance of the phenomenon in question, even in low-precision queries. The classifier is trained in an in-tool fashion, except for preprocessing relying only on the manual annotations done by the users in the querying tool itself. To implement this approach, we build upon CSniper1, a web-based multi-user search and annotation tool.

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Erik-Lân Do Dinh, Richard Eckart de Castilho, Iryna Gurevych
• Keywords: ACL 2015, Computational Linguistics