Exploring convolutional neural networks for drug–drug interaction extraction
نویسندگان
چکیده
منابع مشابه
Exploring convolutional neural networks for drug–drug interaction extraction
Drug-drug interaction (DDI), which is a specific type of adverse drug reaction, occurs when a drug influences the level or activity of another drug. Natural language processing techniques can provide health-care professionals with a novel way of reducing the time spent reviewing the literature for potential DDIs. The current state-of-the-art for the extraction of DDIs is based on feature-engine...
متن کاملDrug-Drug Interaction Extraction via Convolutional Neural Networks
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 learning method which almost does not need ...
متن کاملRelation Extraction: Perspective from Convolutional Neural Networks
Up to now, relation extraction systems have made extensive use of features generated by linguistic analysis modules. Errors in these features lead to errors of relation detection and classification. In this work, we depart from these traditional approaches with complicated feature engineering by introducing a convolutional neural network for relation extraction that automatically learns feature...
متن کاملExploring Convolutional Neural Networks for Sentiment Analysis of Spanish tweets
Spanish is the third-most used language on the Internet, after English and Chinese, with a total of 7.7% of Internet users (more than 277 million of users) and a huge users growth of more than 1,400%. However, most work on sentiment analysis has focused on English. This paper describes a deep learning system for Spanish sentiment analysis. To the best of our knowledge, this is the first work th...
متن کاملDistant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks
Two problems arise when using distant supervision for relation extraction. First, in this method, an already existing knowledge base is heuristically aligned to texts, and the alignment results are treated as labeled data. However, the heuristic alignment can fail, resulting in wrong label problem. In addition, in previous approaches, statistical models have typically been applied to ad hoc fea...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Database
سال: 2017
ISSN: 1758-0463
DOI: 10.1093/database/bax019