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Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features. Recently, convolutional neural networks (CNN), a robust machine(More)
Drug name recognition (DNR) is a critical step for drug information extraction. Machine learning-based methods have been widely used for DNR with various types of features such as part-of-speech, word shape, and dictionary feature. Features used in current machine learning-based methods are usually singleton features which may be due to explosive features(More)
Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are(More)
Drug name recognition (DNR), which seeks to recognize drug mentions in unstructured medical texts and classify them into pre-defined categories, is a fundamental task of medical information extraction, and is a key component of many medical relation extraction systems and applications. A large number of efforts have been devoted to DNR, and great progress(More)
A pilot-scale biofilter treating real groundwater was developed in this study, which showed that ammonia, iron and manganese were mainly removed at 0.4, 0.4 and 0.8 m of the filter bed, respectively, and the corresponding removal efficiencies were 90.82%, 95.48% and 95.90% in steady phase, respectively. The variation of microbial populations in the(More)
With the rapid growth of information on the Internet, many Single-Pass based clustering methods are used in topic detection and tracking (TDT) because of Single-Pass's characteristics of incremental processing. In Single-Pass based methods, similarities between the feature vectors of news reports and the cluster centers of historical topics are calculated.(More)
Microblog has become one of the most popular social networking services. Users of Microblog generate large amounts of data everyday. Volumes of data and the large number of users make Microblog potentially a valuable data source for data mining and knowledge discovery. This paper aims to mine knowledge from the user generated data and discover the major(More)
Microblog, as an online communication platform, is becoming more and more popular. Users generate volumes of data everyday and the user generated content contains a lot of useful knowledge such as practical skills and technical expertise. This paper proposes a cross-data method to mine recipes in Microblog. In the proposed method, snippets of text relevant(More)
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