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The UET-CAM System in the BioCreAtIvE V CDR Task

Le, Hoang Quynh and Tran, Mai Vu and Dang, Thanh Hai and Nigel, Collier (2015) The UET-CAM System in the BioCreAtIvE V CDR Task. In: 2015: the 5th BioCreative challenge evaluation workshop, 2015, Sevilla, Spain.

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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

Item Type: Conference or Workshop Item (Paper)
Subjects: Information Technology (IT)
Divisions: Faculty of Information Technology (FIT)
Depositing User: Ms. Thi Minh Chu
Date Deposited: 01 Jun 2016 13:31
Last Modified: 01 Jun 2016 13:31

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