Using Associate Constraint Network with Forward Evaluation to Overcome Cross-lingual Semantic Interoperability Challenge for Crime Information Extraction

Information extraction is important for crime analysis. Due to the popularity of the Web, information related to crime and terrorism is available in multiple languages. As a result, cross-lingual semantic interoperability is essential when we extract information across multiple languages. In our previous work, we have developed several techniques to generate an automatic cross-lingual thesaurus to support cross-lingual information retrieval based on a parallel corpus collected from the Web. The techniques include Hopfield network and associate constraint network with backmarking. Although these techniques obtain satisfactory performance, they have weaknesses in efficiency, consistency, precision or recall. In this work, we develop a new searching technique, namely forward evaluation, on the basis of our previously developed associate constraint network model. We have conducted an experiment and show that the proposed forward evaluation technique outperforms both Hopfield network and associate constraint network with backmarking in terms of precision and recall. In addition, its efficiency is better than Hopfield network but is not as good as associate constraint network with backmarking.