Information Extraction from Amharic Language Text: Knowledge-Poor Approach
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Date
2015-06
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Addis Ababa University
Abstract
During the last two decades with the accelerated Internet development a great amount of data
have been being accumulated and stored on the Web. We are drowns with much data at office,
home either in printable or electronic form. Then finding the relevant information from this mass
data is critical. At this end, information extraction is a technology which creates the structured
representation of unstructured texts by extracting relevant entities from them, thereby, making
the data analysis realizable.
This work focuses on developing information extraction system from Amharic language text.
The proposed system developed using GATE (General Architecture for Text Engineering) text
processing environment using knowledge-poor approach on infrastructure domain. By
knowledge-poor approach we mean we are using simple rules and gazetteer list for entity
identification. Our proposed Amharic text information extractor consists of three phase’s
namely preprocessing, extraction and post processing. The preprocessing phases used for
handling language specific issues and setting the environment ready for extraction process. The
second phase is the main unit in our model. It basically performs named entity recognition,
coreference resolution and relation extraction and extract relevant text. The post processing step
annotates the selected data and presents the extracted information in a structured form.
Various evaluation techniques, which are used to evaluate the performance of our proposed
model were used. The usual precision, recall and F-measure were used to measure the efficiency
of the proposed work. We have used 24760 instances for training and testing our model. Our
evaluation was conducted on name entity recognition component separately and the overall
system as information extraction component. Accordingly, the system achieves the F-measure of
89.1 % on the named entity recognition and in the overall it achieves the F-measure of 89.8%.
Key words: Information Extraction, Amharic Text Information Extraction, Coreference
Resolution, Relation Extraction, GATE
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Keywords
Information Extraction; Amharic Text Information Extraction; Coreference Resolution; Relation Extraction; Gate