13,309 research outputs found
Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension
Reading comprehension is a challenging task in natural language processing
and requires a set of skills to be solved. While current approaches focus on
solving the task as a whole, in this paper, we propose to use a neural network
`skill' transfer approach. We transfer knowledge from several lower-level
language tasks (skills) including textual entailment, named entity recognition,
paraphrase detection and question type classification into the reading
comprehension model.
We conduct an empirical evaluation and show that transferring language skill
knowledge leads to significant improvements for the task with much fewer steps
compared to the baseline model. We also show that the skill transfer approach
is effective even with small amounts of training data. Another finding of this
work is that using token-wise deep label supervision for text classification
improves the performance of transfer learning
Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD-10
diagnostic codes. We developed separate machine learning models that can handle
data from different modalities, including unstructured text, semi-structured
text and structured tabular data. We further employed an ensemble method to
integrate all modality-specific models to generate ICD-10 codes. Key evidence
was also extracted to make our prediction more convincing and explainable. We
used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset
to validate our approach. For ICD code prediction, our best-performing model
(micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other
baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and
Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability,
our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text
data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780
and 0.5002 respectively.Comment: Machine Learning for Healthcare 201
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Neural networks have recently been proposed for multi-label classification
because they are able to capture and model label dependencies in the output
layer. In this work, we investigate limitations of BP-MLL, a neural network
(NN) architecture that aims at minimizing pairwise ranking error. Instead, we
propose to use a comparably simple NN approach with recently proposed learning
techniques for large-scale multi-label text classification tasks. In
particular, we show that BP-MLL's ranking loss minimization can be efficiently
and effectively replaced with the commonly used cross entropy error function,
and demonstrate that several advances in neural network training that have been
developed in the realm of deep learning can be effectively employed in this
setting. Our experimental results show that simple NN models equipped with
advanced techniques such as rectified linear units, dropout, and AdaGrad
perform as well as or even outperform state-of-the-art approaches on six
large-scale textual datasets with diverse characteristics.Comment: 16 pages, 4 figures, submitted to ECML 201
Co-training for Demographic Classification Using Deep Learning from Label Proportions
Deep learning algorithms have recently produced state-of-the-art accuracy in
many classification tasks, but this success is typically dependent on access to
many annotated training examples. For domains without such data, an attractive
alternative is to train models with light, or distant supervision. In this
paper, we introduce a deep neural network for the Learning from Label
Proportion (LLP) setting, in which the training data consist of bags of
unlabeled instances with associated label distributions for each bag. We
introduce a new regularization layer, Batch Averager, that can be appended to
the last layer of any deep neural network to convert it from supervised
learning to LLP. This layer can be implemented readily with existing deep
learning packages. To further support domains in which the data consist of two
conditionally independent feature views (e.g. image and text), we propose a
co-training algorithm that iteratively generates pseudo bags and refits the
deep LLP model to improve classification accuracy. We demonstrate our models on
demographic attribute classification (gender and race/ethnicity), which has
many applications in social media analysis, public health, and marketing. We
conduct experiments to predict demographics of Twitter users based on their
tweets and profile image, without requiring any user-level annotations for
training. We find that the deep LLP approach outperforms baselines for both
text and image features separately. Additionally, we find that co-training
algorithm improves image and text classification by 4% and 8% absolute F1,
respectively. Finally, an ensemble of text and image classifiers further
improves the absolute F1 measure by 4% on average
- …