Lightweight Morphological Analysis Model for Smart Home Applications Based on Natural Language Interfaces

Abstract

With the rapid evolution of the smart home environment, the demand for natural language processing (NLP) applications on information appliances is increasing. However, it is not easy to embed NLP-based applications in information appliances because most information appliances have hardware constraints such as small memory, limited battery capacity, and restricted processing power. In this paper, we propose a lightweight morphological analysis model, which provides the first step module of NLP for many languages. To overcome hardware constraints, the proposed model modifies a well-known left-longest-match-preference (LLMP) model and simplifies a conventional hidden Markov model (HMM). In the experiments, the proposed model exhibited good performance (a response time of 0.0195 sec per sentence, a memory usage of 1.85 MB, a precision of 92%, and a recall rate of 90%) in terms of the various evaluation measures. On the basis of these experiments, we conclude that the proposed model is suitable for natural language interfaces of information appliances with many hardware limitations because it requires less memory and consumes less battery power.