Abstract: This study aims to develop a Named Entity Recognition (NER) that creates new tags that facilitate fast query processing, information retrieval and data preprocessing of Travel and Tourism Domain. We have used a machine learning approach that uses domain specific knowledge to train the data and label the entities with appropriate tags. Conditional Random Fields (CRF) is implemented and used to train the input domain specific data that yields good performance. Experimental and evaluation result shows that the learned model yields a travel and tourism domain specific NER recall of 82%, precision of 85%, accuracy of 82% and F-measure of 83%. Thus learned CRF model builds a domain specific NER for tourism, travel, hotel and point of Interest domain and tags the domain keywords with appropriate tags.
Jobi Vijay and Rajeswari Sridhar, 2016. A Machine Learning Approach to Named Entity Recognition for the Travel and Tourism Domain. Asian Journal of Information Technology, 15: 4309-4317.