AUTOMATIC RELATION EXTRACTION BETWEEN ENTITIES FOR AMHARIC TEXT

This research work primarily focused on the automatic relation extraction between entities for Amharic text using supervised machine learning approach.The Walta Information Centre online archive resources were used to create the studys own corpus, which consisted of 2000 sentences and a reasonable quantity of 30,466 words or tokens. The proposed solution has four processes namely preprocessing, Text labeling, feature extraction and feature selection and Recognition. The tokenization and POS are used as preprocessing. After the text is tokenized and giving POS for each of tokens the next step is text labeling system. For text labeling mechanism BIO scheme is used. The tag features are selected for building the model. The Tag feature consists of name entity type and relation type. The name entity type features are represented by Location (LOC), Organization (ORG) and Person (PER) and the relation type features are identified every word which existed between two entities for instance between location-location relation type or location-organization relation type and all the corresponding entities that are appeared it. Vectorizations are done using DictVectorizer and word2features. Supp