  When patients need to take medicine, particularly taking more than one kind
of drug simultaneously, they should be alarmed that there possibly exists
drug-drug interaction. Interaction between drugs may have a negative impact on
patients or even cause death. Generally, drugs that conflict with a specific
drug (or label drug) are usually described in its drug label or package insert.
Since more and more new drug products come into the market, it is difficult to
collect such information by manual. We take part in the Drug-Drug Interaction
(DDI) Extraction from Drug Labels challenge of Text Analysis Conference (TAC)
2018, choosing task1 and task2 to automatically extract DDI related mentions
and DDI relations respectively. Instead of regarding task1 as named entity
recognition (NER) task and regarding task2 as relation extraction (RE) task
then solving it in a pipeline, we propose a two step joint model to detect DDI
and it's related mentions jointly. A sequence tagging system (CNN-GRU
encoder-decoder) finds precipitants first and search its fine-grained Trigger
and determine the DDI for each precipitant in the second step. Moreover, a rule
based model is built to determine the sub-type for pharmacokinetic interation.
Our system achieved best result in both task1 and task2. F-measure reaches 0.46
in task1 and 0.40 in task2.
