Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for th...

Aggressive text detection in social networks allows to identify offenses and misbehavior, and leverages tasks such as cyberbullying detection. We propose to automatically map a document with an aggressiveness score (thus treating aggressive text detection as a regression problem) and explore different approaches for this purpose. These include lexiconbased, supervised, fuzzy, and statistical ap...

Evidentiality is the linguistic representation of the nature of evidence for a statement. In other words, it is the linguistically encoded evidence for the trustworthiness of a statement. In this paper, we aim to explore how linguistically encoded information of evidentiality can contribute to the prediction of trustworthiness in natural language processing (NLP). We propose to incorporate evid...

Here in this paper a new and efficient technique for the text detection from natural scenes is implemented. The proposed methodology is based on the concept of Otsu’s segmentation method which segments the higher intensity texts from the natural scenes. Although there are various text detection techniques implemented, but the proposed methodology implemented here for text detection provides hig...

In this paper analysis and comparison of various methods for text detection is carried by using canny edge detection algorithm and MSER based method along with the image enhancement which results in the improved performance in terms of text detection. In addition, we improve current MSERs by developing a contrast enhancement mechanism that enhances region stability of text patterns to remove th...

This paper propose a topical text segmentation method based on intended boundaries detection and compare it to a well known default boundaries detection method, c99. We ran the two methods on a corpus of twenty two French political discourses and results showed us that intended boundaries detection is better than default boundaries detection on well structured text.

Text detection and recognition in poor quality video is a challenging problem due to unpredictable blur and distortion effects caused by camera and text movements. This affects the overall performance of the text detection and recognition methods. This paper presents a combined quality metric for estimating the degree of blur in the video/image. Then the proposed method introduces a blind decon...

The detection and recognition of text from unconstrained, general-purpose video is an important research problem with multiple applications in the surveillance, archiving and content-based retrieval contexts. Many text detection and localization algorithms have been proposed in the literature. However many of these algorithms either make simplistic assumptions as to the nature of the text to be...

Recent work on error detection has shown that the quality of manually annotated corpora can be substantially improved by applying consistency checks to the data and automatically identifying incorrectly labelled instances. These methods, however, can not be used for automatically annotated corpora where errors are systematic and cannot easily be identified by looking at the variance in the data...