부산대학교 도서관

Computational natural language processing (NLP) is indispensable in a humanized ambience intelligence environment. NLP facilitates ambient intelligence by making machines understand, and be understood by, humans. This in turn makes machines behave more human-like than they typically are today. Technological advances in machine learning (ML), and especially deep learning (DL), have been a key enabler of NLP research. This paper begins with a survey of recent developments of ML/DL for NLP. It then identifies some of the most promising techniques reported in recent literature. These most promising techniques are then assembled into a reusable toolkit for computational NLP. The adaptable nature of the assembled toolkit allows it to be reused in a broad range of NLP applications. The paper then describes experimental evaluation of our implemented solutions for comparative analysis. Two specific NLP applications form the basis of comparative evaluation. The first involves identifying one of M English sentences that does not make sense. The second, which is harder than the first, involves choosing from among N sentences the one that best explains why a presented sentence is invalid. Human baseline accuracies for these applications are 99.1% and 97.8%, respectively. The observation that these results are somewhat less than perfect demonstrates that even humans can occasionally find these tasks difficult. It further underscores the difficulties involved in some of these computational NLP applications. Experiments conducted on benchmark data show that advanced ML/DL can achieve near-human performance in both computational NLP applications with accuracy scores of 96.1% and 93.7%, respectively.