A maximum entropy approach to natural language processing
Computational Linguistics
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
The Journal of Machine Learning Research
Vector-based natural language call routing
Computational Linguistics
Empirical estimates of adaptation: the chance of two noriegas is closer to p/2 than p2
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic analysis of call-center conversations
Proceedings of the 14th ACM international conference on Information and knowledge management
Computational measures for language similarity across time in online communities
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
Toward joint segmentation and classification of dialog acts in multiparty meetings
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Knowledge discovery in time series databases
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
We introduce the relative rank differential statistic which is a non-parametric approach to document and dialog analysis based on word frequency rank-statistics. We also present a simple method to establish semantic saliency in dialog, documents, and dialog segments using these word frequency rank statistics. Applications of our technique include the dynamic tracking of topic and semantic evolution in a dialog, topic detection, automatic generation of document tags, and new story or event detection in conversational speech and text. Our approach benefits from the robustness, simplicity and efficiency of non-parametric and rank based approaches and consistently outperformed term-frequency and TF-IDF cosine distance approaches in several experiments conducted.