Next Improvement Towards Linear Named Entity Recognition Using Character Gazetteers

Natural Language Processing (NLP) is important and interesting area in computer science affecting also other spheres of science; e.g., geographical processing, social statistics, molecular biology. A large amount of textual data is continuously produced in media around us and therefore there is a need of processing it in order to extract required information. One of the most important processing steps in NLP is Named Entity Recognition (NER), which recognizes occurrence of known entities in input texts. Recently, we have already presented our approach for linear NER using gazetteers, namely Hash-map Multi-way Tree (HMT) and first-Child next-Sibling binary Tree (CST) with their strong and weak sides. In this paper, we present Patricia Hash-map Tree (PHT) character gazetteer approach, which shows as the best compromise between the both previous versions according to matching time and memory consumption.