Representing Overlaps in Sequence Labeling Tasks with a Novel Tagging Scheme: bigappy-unicrossy. Berk, G., Erden, B., & Güngör, T. In 2019.
Representing Overlaps in Sequence Labeling Tasks with a Novel Tagging Scheme: bigappy-unicrossy [pdf]Paper  Representing Overlaps in Sequence Labeling Tasks with a Novel Tagging Scheme: bigappy-unicrossy [link]Website  abstract   bibtex   
Multiword expression (MWE) identification can be handled by using sequence tagging approach accompanied with stochastic models and variants of IOB tagging scheme. In this paper, we introduce a new tagging scheme called bigappy-unicrossy to rise to the challenge of overlapping MWEs. The bigappy-unicrossy tagging scheme is compared with the two other well-known tagging schemes which are IOB2 and gappy 1-level in the verbal multiword expression (VMWE) identification task using bidirectional Long Short-Term Memory model with a Conditional Random Field layer on top (bidirectional LSTM-CRF). Both the bigappy-unicrossy and the gappy 1-level tagging schemes outperform the IOB2 tagging scheme. The bigappy-unicrossy tagging scheme competes with the gappy 1-level tagging scheme. We believe that our tagging scheme will show better performance on corpora with higher frequency of overlapping cases.

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