Search and analysis of bankruptcy cause by classification network
MEDI'11 Proceedings of the First international conference on Model and data engineering
Automatic text summarization based on lexical chains
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Extraction of shallow language patterns: an approximation of data oriented parsing
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Extending machine translation evaluation metrics with lexical cohesion to document level
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
| Hi-index | 0.00 |
Most current information retrieval systems rely solely on lexical item repetition, which is notorious for its vulnerability. In this research, we propose a novel method for the extraction of salient textual patterns. One of our major objectives is to move away from keywords and their associated limitations in textual information retrieval. How individual sentences in text fit together to be perceived as a salient pattern is identified. A text network that exhibits textual continuity, arising from a connectionist model, is described. The network facilitates a dynamic extraction of salient textual segments by capturing semantics from two different categories of natural language, namely lexical cohesion and contextual coherence. We also present the results of an empirical study designed to compare our model with the performance of human judges in the identification of salient textual patterns. The preliminary results show that our model has the potential for automatic salient patterns discovery in text.