5 research outputs found
Opinion Mining Summarization and Automation Process: A Survey
In this modern age, the internet is a powerful source of information. Roughly, one-third of the world population spends a significant amount of their time and money on surfing the internet. In every field of life, people are gaining vast information from it such as learning, amusement, communication, shopping, etc. For this purpose, users tend to exploit websites and provide their remarks or views on any product, service, event, etc. based on their experience that might be useful for other users. In this manner, a huge amount of feedback in the form of textual data is composed of those webs, and this data can be explored, evaluated and controlled for the decision-making process. Opinion Mining (OM) is a type of Natural Language Processing (NLP) and extraction of the theme or idea from the user's opinions in the form of positive, negative and neutral comments. Therefore, researchers try to present information in the form of a summary that would be useful for different users. Hence, the research community has generated automatic summaries from the 1950s until now, and these automation processes are divided into two categories, which is abstractive and extractive methods. This paper presents an overview of the useful methods in OM and explains the idea about OM regarding summarization and its automation process
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Large scale machine learning and deep models are extremely data-hungry.
Unfortunately, obtaining large amounts of labeled data is expensive, and
training state-of-the-art models (with hyperparameter tuning) requires
significant computing resources and time. Secondly, real-world data is noisy
and imbalanced. As a result, several recent papers try to make the training
process more efficient and robust. However, most existing work either focuses
on robustness or efficiency, but not both. In this work, we introduce Glister,
a GeneraLIzation based data Subset selecTion for Efficient and Robust learning
framework. We formulate Glister as a mixed discrete-continuous bi-level
optimization problem to select a subset of the training data, which maximizes
the log-likelihood on a held-out validation set. Next, we propose an iterative
online algorithm Glister-Online, which performs data selection iteratively
along with the parameter updates and can be applied to any loss-based learning
algorithm. We then show that for a rich class of loss functions including
cross-entropy, hinge-loss, squared-loss, and logistic-loss, the inner discrete
data selection is an instance of (weakly) submodular optimization, and we
analyze conditions for which Glister-Online reduces the validation loss and
converges. Finally, we propose Glister-Active, an extension to batch active
learning, and we empirically demonstrate the performance of Glister on a wide
range of tasks including, (a) data selection to reduce training time, (b)
robust learning under label noise and imbalance settings, and (c) batch-active
learning with several deep and shallow models. We show that our framework
improves upon state of the art both in efficiency and accuracy (in cases (a)
and (c)) and is more efficient compared to other state-of-the-art robust
learning algorithms in case (b)