RULE-BASED NAMED ENTITY RECOGNITION FOR GREEK FINANCIAL TEXTS

The identification and classification of proper names (named entity recognition) is considered an important task in the area of Information Retrieval and Extraction. A typical named entity recognition (NER) system mainly consists of a lexicon and a grammar. When moving to a new domain, these lexical resources should be customised, either manually or exploiting machine learning techniques. In this paper, we present a NER system based on hand crafted lexical resources. The system is part of a Greek information extraction system and was tested on a Greek corpus of financial news with satisfactory results.