TY  - JOUR
T1  - Mention Detection with Pointer Networks
AU  - Park, Cheoneum 
AU  - Lee, Changki 
JO  - Journal of KIISE, JOK
PY  - 2017
DA  - 2017/1/14
DO  - 10.5626/JOK.2017.44.8.774
KW  - pointer networks
KW  - mention detection
KW  - deep learning
KW  - coreference resolution
AB  - Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term “mention detection” relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.