Dependency-based long short term memory network for drug-drug interaction extraction

Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network

A drug can affect the activity of other drugs, when administered together, in both synergistic or antagonistic ways. In one hand synergistic effects lead to improved therapeutic outcomes, antagonistic consequences can be life-threatening, leading to increased healthcare cost, or may even cause death. Thus, identification of unknown drug-drug interaction (DDI) is an important concern for efficie...

Extraction of Drug-Drug Interaction from Literature through Detecting Linguistic-based Negation and Clause Dependency

Extracting biomedical relations such as drug-drug interaction (DDI) from text is an important task in biomedical NLP. Due to the large number of complex sentences in biomedical literature, researchers have employed some sentence simplification techniques to improve the performance of the relation extraction methods. However, due to difficulty of the task, there is no noteworthy improvement in t...

Transition-Based Dependency Parsing with Stack Long Short-Term Memory

We propose a technique for learning representations of parser states in transitionbased dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks— the stack LSTM. Like the conventional stack data structures used in transitionbased parsing, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LST...

Dependency-based Siamese long short-term memory network for learning sentence representations

Textual representations play an important role in the field of natural language processing (NLP). The efficiency of NLP tasks, such as text comprehension and information extraction, can be significantly improved with proper textual representations. As neural networks are gradually applied to learn the representation of words and phrases, fairly efficient models of learning short text representa...

Short-Long-Term Memory Interaction with Underlearned Long-Term Storage.