A Joint Neural Model for Information Extraction with Global Features
Most existing joint neural models for Information Extraction (IE) use local task specific classifiers to predict labels for individual instances (e.g. trigger, relation) regardless of their interactions. For example, a victim of a die event is likely to be a victim of an attack event in the same sentence. In order to capture such cross subtask and cross instance inter dependencies, we propose a joint neural framework, OneIE, that aims to extract the globally optimal IE result as a graph from an input sentence. OneIE performs end to end IE in four stages: (1) Encoding a given sentence as contextualized word representations; (2) Identifying entity mentions and event triggers as nodes; (3) Computing label scores for all nodes and their pairwise links using local classifiers; (4) Searching for the globally optimal graph with a beam decoder. At the decoding stage, we incorporate global features to capture the cross subtask and cross instance interactions. Experiments show that adding global features improves the performance of our model and achieves new state of the art on all subtasks. In addition, as OneIE does not use any language specific feature, we prove it can be easily applied to new languages or trained in a multilingual manner.
ACL