7,272 research outputs found
Exploiting Deep Neural Networks for Intention Mining
© 2020 ACM. In the current era of digital media, people are greatly interested to express themselves on online interaction which produces a huge amount of data. The user generated content may contain user\u27s emotions, opinions, daily events and specially their intent or motive behind their communication. Intention identification/mining of user\u27s reviews, that is whether a user review contains intent or not, from social media network, is an emerging area and is in great demand in various fields like online advertising, improving customer services and decision making. Until now, a lot of work has been performed by researchers on user intention identification using machine learning approaches. However, it is demanded to focus on deep neural network methods. In this research work, we have conducted experimentation on intention dataset using a deep learning method namely CNN+BILSTM. The results exhibit that the proposed model efficiently performed identification of intention sentences in user generated text with a 90% accuracy
WordSup: Exploiting Word Annotations for Character based Text Detection
Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a common text detection engine based on character
detectors. However, training character detectors requires a vast of location
annotated characters, which are expensive to obtain. Actually, the existing
real text datasets are mostly annotated in word or line level. To remedy this
dilemma, we propose a weakly supervised framework that can utilize word
annotations, either in tight quadrangles or the more loose bounding boxes, for
character detector training. When applied in scene text detection, we are thus
able to train a robust character detector by exploiting word annotations in the
rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The
character detector acts as a key role in the pipeline of our text detection
engine. It achieves the state-of-the-art performance on several challenging
scene text detection benchmarks. We also demonstrate the flexibility of our
pipeline by various scenarios, including deformed text detection and math
expression recognition.Comment: 2017 International Conference on Computer Visio
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