A Neural Joint Model for Extracting Bacteria and Their Locations

Extracting Lives In relations between bacteria and their locations involves two steps, namely bacteria/location entity recognition and Lives In relation classification. Previous work solved this task by pipeline models, which may suffer error propagation and cannot utilize the interactions between these steps. We follow the line of work using joint models, which perform two subtasks simultaneously to obtain better performances. A state-of-the-art neural joint model for relation extraction in the Automatic Content Extraction (ACE) task is adapted to our task. Furthermore, we propose two strategies to improve this model. First, a novel relation is suggested in the second step to detect the errors in the first step, thus this relation can correct some errors in the first step. Second, we replace the original greedy-search decoding with beamsearch, and train the model with early-update techniques. Experimental results on a standard dataset for this task show that our adapted model achieves better precisions than other systems. After adding the novel relation, we gain a nearly 2% improvement of F1 for Lives In relation extraction. When beam-search is used, the F1 is further improved by 6%. These demonstrate that our proposed strategies are effective for this task. However, additional experiments show that the performance improvement in another dataset of bacteria and location extraction is not significant. Therefore, whether our methods are effective for other relation extraction tasks needs to be further investigated.