Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually. However, traditional sentence classification approaches have been shown to greatly benefit from jointly classifying subsequent sentences, such as with conditional random fields. In this work, we present an ANN architecture that combines the effectiveness of typical ANN models to classify sentences in isolation, with the strength of structured prediction. Our model outperforms the state-ofthe- art results on two different datasets for sequential sentence classification in medical abstracts
In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent ...
Background: Biomedical literature is expanding rapidly, and tools that help locate information of in...
Natural language processing systems which deal with real-world documents require several low-level t...
© 2018 Association for Computational Linguistics Prevalent models based on artificial neural network...
We introduce a family of deep-learning architectures for inter-sentence relation extraction, i.e., r...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
Abstract Aim Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim t...
Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field o...
Aim: Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automa...
Aim: Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automa...
Abstract Background Biomedical literature is expanding rapidly, and tools that help locate informati...
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automat...
The classification of abstract sentences is a valuable tool to support scientific database querying,...
In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent ...
Background: Biomedical literature is expanding rapidly, and tools that help locate information of in...
Natural language processing systems which deal with real-world documents require several low-level t...
© 2018 Association for Computational Linguistics Prevalent models based on artificial neural network...
We introduce a family of deep-learning architectures for inter-sentence relation extraction, i.e., r...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
Abstract Aim Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim t...
Convolutional Neural Networks (CNNs) and pre-trained word embeddings have revolutionized the field o...
Aim: Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automa...
Aim: Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automa...
Abstract Background Biomedical literature is expanding rapidly, and tools that help locate informati...
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automat...
The classification of abstract sentences is a valuable tool to support scientific database querying,...
In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent ...
Background: Biomedical literature is expanding rapidly, and tools that help locate information of in...
Natural language processing systems which deal with real-world documents require several low-level t...