A Neural Network for Coordination Boundary Prediction
Table 3: Recall on the Beta version of Genia corpus. Numbers for Hara et al. are taken from their paper.
['COOD', '#', 'Our Model', 'Hara et al.']
Following Hara et al. \shortcitehara2009coordinate, we evaluate the models’ ability to predict the span of the entire coordination phrase, disregarding the individual conjuncts. For example, in “My plan is to visit Seychelles, ko Samui and Sardinia by the end of the year” the goal is to recover “Seychelles, ko Samui and Sardinia”. This is a recall measure. We follow the exact protocol of Hara et al. \shortcitehara2009coordinate and train and evaluate the model on 3598 coordination phrases in Genia Treebank Beta and report the micro-averaged results of a five-fold cross validation run. As shown by Hara et al. \shortcitehara2009coordinate, syntactic parsers do not perform well on the Genia treebank. Thus, in our symmetry component we opted to not rely on predicted tree structures, and instead use the simpler option of representing each conjunct by its sequence of POS tags. To handle coordination phrases with more than two conjuncts, we extract candidates which includes up to 7 spans and integrate the first and the last span in the model features. Like Hara et al., we use gold POS. Our proposed model achieves Recall score of 64.14 (2.64 Recall points gain over Hara et al.) and significantly improves the score of several coordination types.