@inproceedings{SisLab1610,
       booktitle = {2015: the 5th BioCreative challenge evaluation workshop},
           title = {The UET-CAM System in the BioCreAtIvE V CDR Task},
          author = {Hoang Quynh Le and Mai Vu Tran and Thanh Hai Dang and Collier Nigel},
            year = {2015},
           pages = {208--214},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1610/},
        abstract = {In this paper, we describe a system developed for the BioCreative Vchemical-disease relation (CDR) task. The Disease Named Entity Recognition and Normalization (DNER) model employs joint learning using a perceptronbased
named entity recognizer (NER) and a back-off model for named entity normalization (NEN). In order to maximize both precision and recall, our NEN adopts a sequential back-off ensemble approach based on Semantic Supervised
Indexing (SSI) - a supervised Word Embedding (WE) method- giving results by inference from training data, and Skip-gram - an unsupervised WE methodtaking advantage of large unlabeled data. In the Chemical-induced diseases relation
extraction (CID) model, we firstly resolve co-references by using a multipass sieve to identify cross-sentence references for entities, thus enabling intrasentence
relations to be discovered more easily. Following this we extract CID relations using a support vector machine model trained on supervised sentence data from the CDR training and development dataset. We evaluated our method
on both the DNER test set and the CID test set. Results show an F1 of 76.44 for the DNER tas}
}