Model Architectures for Quotation Detection
Christian Scheible, Roman Klinger, Sebastian Padó · 2016
Quotation 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.