© 2018 Association for Computational Linguistics Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medica...
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automat...
We present a biologically inspired computational framework for language processing and grammar acqui...
Recent years have witnessed increasing interests in developing interpretable models in Natural Langu...
Existing models based on artificial neural networks (ANNs) for sentence classification often do not ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Modeling discourse coherence is an important problem in natural language generation and understandin...
In the sentence classification task, context formed from sentences adjacent to the sentence being cl...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
We present a biologically inspired computational framework for language processing and grammar acqui...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
We introduce a family of deep-learning architectures for inter-sentence relation extraction, i.e., r...
Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a g...
Abstract Background Biomedical literature is expanding rapidly, and tools that help locate informati...
Supervised k nearest neighbour and unsupervised hierarchical agglomerative clustering algorithm can ...
Abstract Aim Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim t...
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automat...
We present a biologically inspired computational framework for language processing and grammar acqui...
Recent years have witnessed increasing interests in developing interpretable models in Natural Langu...
Existing models based on artificial neural networks (ANNs) for sentence classification often do not ...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Modeling discourse coherence is an important problem in natural language generation and understandin...
In the sentence classification task, context formed from sentences adjacent to the sentence being cl...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
We present a biologically inspired computational framework for language processing and grammar acqui...
The automatic classification of abstract sentences into its main elements (background, objectives, m...
We introduce a family of deep-learning architectures for inter-sentence relation extraction, i.e., r...
Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a g...
Abstract Background Biomedical literature is expanding rapidly, and tools that help locate informati...
Supervised k nearest neighbour and unsupervised hierarchical agglomerative clustering algorithm can ...
Abstract Aim Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim t...
AIM Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automat...
We present a biologically inspired computational framework for language processing and grammar acqui...
Recent years have witnessed increasing interests in developing interpretable models in Natural Langu...