Yunchuan Chen

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Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichan-nel recurrent neural networks, with(More)
Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichan-nel recurrent neural networks, with(More)
Nowadays, neural networks play an important role in the task of relation classification. By designing different neural architec-tures, researchers have improved the performance to a large extent, compared with traditional methods. However, existing neural networks for relation classification are usually of shallow architectures (e.g., one-layer convolu-tion(More)
This paper aims to compare different reg-ularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neu-ral models and tasks as our testbed. We tried several frequently applied or newly proposed regularization strategies, including penalizing weights (embeddings(More)
Neural networks are among the state-of-the-art techniques for language modeling. Existing neural language models typically map discrete words to distributed, dense vector representations. After information processing of the preceding context words by hidden layers, an output layer estimates the probability of the next word. Such approaches are time-and(More)
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