7,238 research outputs found
A Comparative Study on Regularization Strategies for Embedding-based Neural Networks
This paper aims to compare different regularization strategies to address a
common phenomenon, severe overfitting, in embedding-based neural networks for
NLP. We chose two widely studied neural models and tasks as our testbed. We
tried several frequently applied or newly proposed regularization strategies,
including penalizing weights (embeddings excluded), penalizing embeddings,
re-embedding words, and dropout. We also emphasized on incremental
hyperparameter tuning, and combining different regularizations. The results
provide a picture on tuning hyperparameters for neural NLP models.Comment: EMNLP '1
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path
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; multichannel recurrent neural networks, with long short term memory
(LSTM) units, pick up heterogeneous information along the SDP. Our proposed
model has several distinct features: (1) The shortest dependency paths retain
most relevant information (to relation classification), while eliminating
irrelevant words in the sentence. (2) The multichannel LSTM networks allow
effective information integration from heterogeneous sources over the
dependency paths. (3) A customized dropout strategy regularizes the neural
network to alleviate overfitting. We test our model on the SemEval 2010
relation classification task, and achieve an -score of 83.7\%, higher than
competing methods in the literature.Comment: EMNLP '1
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