State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by usi…
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169,291 papers · 148 categories
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11 results for “LSTM-CNN”
Neural model parses non-projective dependency trees efficiently.
problem Parsing non-projective dependency trees.
method Probabilistic parsing model using neural representations and Kirchhoff's Matrix-Tree Theorem.
result State-of-the-art parsing performance on nine datasets.
Amobee wins WASSA 2018 emotion prediction with language models and LSTM.
problem Predicting emotions from tweets without explicit mentions.
method Ensemble system of language models and LSTM with CNN attention.
result 1st place with macro F1 score of 0.7145.
New method improves sentence classification using context information.
problem Classifying sentences with limited context information.
method Context-LSTM-CNN method that considers large contexts and long-range dependencies.
result Consistently improves over previous methods on two datasets.
PHI Scrubber removes sensitive patient info from medical notes.
problem Protecting patient privacy in electronic health records.
method Deep learning model with de-convolutional neural network and bi-directional LSTM-CNN.
result Eliminates individually identifiable information from physician notes.
Neural model improves text segmentation accuracy.
problem Manual feature engineering and large memory requirements in text segmentation.
method Attention-based bidirectional LSTM model with CNN sentence embeddings and contextual prediction.
result Improves WinDiff score by ~7% on three benchmark datasets.
Deep neural networks improve music phrase segmentation.
problem Automated melodic phrase detection and segmentation in music.
method Adapted various neural network architectures to symbolic music representation, addressing sparse labeling problem.
result CNN-CRF architecture performs best, offering finer segmentation and faster training.
This paper examines how padding affects LSTM and CNN performance.
problem The impact of padding on LSTM and CNN performance.
method Used a sentiment analysis task with various padding techniques on the same dataset for both LSTM and CNN.
result Suggests the best padding technique for improved performance.
Paper develops a hybrid DNN approach for RUL prediction with adaptive drift.
problem RUL estimation challenges in practice, especially online update and uncertainty quantification.
method Hybrid DNN approach with Wiener-based-degradation model and adaptive drift. LSTM-CNN for trajectory prediction and Bayesian inference for adaptive drift.
result Superior accuracy in RUL prediction demonstrated on turbofan engines data.
MRC-LSTM predicts Bitcoin prices using CNN and LSTM.
problem Predicting Bitcoin price with high volatility and complex factors.
method Combines MRC and LSTM, focusing on multi-scale features and long-term dependencies.
result MRC-LSTM significantly outperforms other models in Bitcoin price prediction.
Paper classifies movie genres using multimodal data.
problem Challenging task of multi-label movie genre classification.
method Created dataset from video clips, subtitles, synopses, and posters. Extracted features using various descriptors. Evaluated using different classifiers and late fusion strategy.
result Best F-Score result of 0.628 achieved by combining LSTM on synopses and CNN on movie trailer frames.