A non-learning approach to spelling correction in web queries
Jason Soo · 2013
We describe an adverse environment spelling correction algorithm, known as Segments. Segments is language and domain independent and does not require any training data. We evaluate Segments' correction rate of transcription errors in web query logs with the state-of-the-art learning approach. We show that in environments where learning approaches are not applicable, such as multilingual documents, Segments has an F1-score within 0.005 of the learning approach.