Swarm Algorithms for NLP - The Case of Limited Training Data

Abstract

The present article describes a novel phrasing model which can be used for segmenting sentences of unconstrained text into syntactically-defined phrases. This model is based on the notion of attraction and repulsion forces between adjacent words. Each of these forces is weighed appropriately by system parameters, the values of which are optimised via particle swarm optimisation. This approach is designed to be language-independent and is tested here for different languages. The phrasing model’s performance is assessed per se, by calculating the segmentation accuracy against a golden segmentation. Operational testing also involves integrating the model to a phrase-based Machine Translation (MT) system and measuring the translation quality when the phrasing model is used to segment input text into phrases. Experiments show that the performance of this approach is comparable to other leading segmentation methods and that it exceeds that of baseline systems.

Knowledge Management Referral System Using Artificial Intelligent Techniques

“…In order to make the Web data not only machine readable but also machine understandable the World Wide Web Consortium proposes the Semantic Web [21][22]23] a sequence of technologies that allow for self-describing content. On the Semantic Web metadata is defined using semantic information usually captured in ontology [24,25].…”