Scheible, ChristianChristianScheibleKlinger, RomanRomanKlinger0000-0002-2014-6619Padó, SebastianSebastianPadó2024-03-132024-03-132016https://fis.uni-bamberg.de/handle/uniba/93925Quotation detection is the task of locating spans of quoted speech in text. The state of the art treats this problem as a sequence labeling task and employs linear-chain conditional random fields. We question the efficacy of this choice: The Markov assumption in the model prohibits it from making joint decisions about the begin, end, and internal context of a quotation. We perform an extensive analysis with two new model architectures. We find that (a), simple boundary classification combined with a greedy prediction strategy is competitive with the state of the art; (b), a semi-Markov model significantly outperforms all others, by relaxing the Markov assumption.engModel Architectures004conferenceobject10.18653/v1/P16-1164https://www.aclanthology.org/P16-1164/