@inProceedings{
 title = {Representing Overlaps in Sequence Labeling Tasks with a Novel Tagging Scheme: bigappy-unicrossy},
 type = {inProceedings},
 year = {2019},
 keywords = {Bigappy-unicrossy tagging scheme ·,Gappy 1-level tagging scheme ·,IOB tagging scheme ·,Long,Memory,Multiword expressions ·,Short-,Term},
 websites = {http://universaldependencies.org/format.html},
 id = {0d514ac1-871e-320c-aa7b-921a2d7ee3ad},
 created = {2019-10-12T16:10:31.242Z},
 accessed = {2019-10-12},
 file_attached = {true},
 profile_id = {1971c810-6732-3a00-9f6b-d217e1a53071},
 group_id = {cbcfbfec-195f-3b99-b6a1-d26e1dd80ff5},
 last_modified = {2019-10-12T16:10:31.339Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {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.},
 bibtype = {inProceedings},
 author = {Berk, Gözde and Erden, Berna and Güngör, Tunga}
}

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