Character Identification on Multi-party Dialogues

This thesis introduces a subtask of entity linking, called character identification, that maps mentions in multiparty conversation to their referent characters. Transcripts of TV shows are collected as the sources of our corpus and automatically annotated with mentions by linguistically-motivated rules. These mentions are manually linked to their referents and disambiguate with abstract referent labels through crowdsourcing. Our annotated corpus comprises 448 scenes from 2 seasons and 46 episodes of the TV show Friends, and shows the inter-annotator agreement of κ = 79.96. For statistical modeling, this task is reformulated as coreference resolution, and experimented with two state-of-the-art systems on our corpus. A novel mention-to-mention ranking model is proposed to provides better mention and mention-pair representations learned from feature groupings of dialogue-specific features After linking coreferent clusters to their referent entity with our proposed rule-based remapping algorithm, the best model gives a purity score of 57.27% on average, which is promising given the challenging nature of this task and our corpus.