Pronoun Resolution Based on Deep Learning
XI Xuefen · Beijing Daxue Xuebao. Zirankexueban · 2014
Because coreference resolution is a fundamental task in natural language process, a coreference resolution system based on Deep Learning model via the deep belief nets(DBN), which is a classifier of a combination of several unsupervised learning networks, named RBM(restricted Boltzmann machine) and a supervised learning network named BP(back-propagation), is proposed to detect and classify the coreference relationships between the anaphor and antecedent. The RBM layers maintain as much information as possible when feature vectors are transferred to next layer. The BP layer is trained to classify the features generated by the last RBM layer. The experiments are conducted on the ACE 2004 English NWIRE corpus and the ACE 2005 Chinese NWIRE corpus. The results show that increasing the number of layers RBM training and joining of abstract layer for feature set are able to improve the performance of coreference resolution system.