ON AMBIGUITY DETECTION AND POSTPROCESSING SCHEMES USING CLUSTER ENSEMBLES

The ambiguity in word senses has been recognized as a major challenge for the information retrieval systems. Hindi language web information retrieval, like other languages, faces the problem of sense ambiguity. The sense ambiguity problem deteriorates the performance of every natural language processing (NLP) application. The performance of Hindi language web information retrieval is also affected by it. In this paper, the author formalized an approach for the disambiguation of the senses to improve the performance of Hindi web information retrieval. Our system works in such a way that ambiguity detection has been performed before disambiguation of web queries. Test samples of 100 queries have been selected. When these queries were subjected to ambiguity detection, we found that 43% of them have been detected unambiguous. After ambiguity detection, the disambiguation approach is followed which is based on HSC (Highest Sense Count). Query disambiguation approach further follows query expansion. The expanded query generates the new result set which results into high precision and high similarity score. The 57 expanded queries are tested against 1000 test document instances. The overall improvement is 45% in the average precision, 23% in interpolated average precision and a significant improvement in the average similarity score of the new generated result set. The overall accuracy of our approach has been 61.4% and it improves the performance of the system by 45%.

Download Full-text

Unsupervised Emotional Scene Detection from Lifelog Videos Using Cluster Ensembles

International Journal of Software Innovation ◽

10.4018/ijsi.2013100101 ◽

2013 ◽

Vol 1 (4) ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Hiroki Nomiya ◽

Atsushi Morikuni ◽

Teruhisa Hochin

Keyword(s):

Facial Expression ◽

Facial Expressions ◽

Facial Expression Recognition ◽

Detection Method ◽

Video Retrieval ◽

Detection Performance ◽

Expression Recognition ◽

Scene Detection ◽

Cluster Ensembles ◽

Emotional Scenes

An emotional scene detection method is proposed in order to retrieve impressive scenes from lifelog videos. The proposed method is based on facial expression recognition considering that a wide variety of facial expression could be observed in impressive scenes. Conventional facial expression techniques, which focus on discriminating typical facial expressions, will be inadequate for lifelog video retrieval because of the diversity of facial expressions. The authors thus propose a more flexible and efficient emotional scene detection method using an unsupervised facial expression recognition based on cluster ensembles. The authors' approach does not need to predefine facial expressions and is able to detect emotional scenes containing a wide variety of facial expressions. The detection performance of the proposed method is evaluated through some emotional scene detection experiments.

Download Full-text