Information extraction model from Ge’ez texts

Nowadays, voluminous and unstructured textual data is found on the Internet that could provide varied valuable information for different institutions such as health care, business-related, training, religion, culture, history, among others. A alarming growth of fosters need various methods techniques to extract from data. However, exploring helpful satisfy needs stakeholders becomes a problem due overload via internet. This paper, therefore, presents an effective model extracting named entities Ge'ez text using deep learning algorithms. set with total 5,270 sentences were used training testing purposes. Two experimental setups, i.e., long short-term memory (LSTM) bidirectional (Bi-LSTM) make empirical evaluation split ratio 80% 20%, respectively. Experimental results showed proposed be practical solution building extraction (IE) systems Bi-LSTM, reaching validation, accuracy high 98.59%, 97.96%, 96.21%, The performance reflect promising compared resource-rich languages English.<!--[if gte mso 9]><xml>
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 <![endif]--><p class="MsoNormal" style="text-align: justify;"><span lang="EN-US">Bi-LSTM;<strong style="mso-bidi-font-weight: normal;"><em style="mso-bidi-font-style: normal;"></em></strong></span></p><p lang="EN-US">Deep learning;</span></p><p lang="EN-US">Entity extraction;</span></p><p lang="EN-US">Ge’ez text;</span></p><span style="font-size: 10.0pt; font-family: 'Times Roman',serif; mso-fareast-font-family: Roman'; mso-ansi-language: EN-US; mso-fareast-language: mso-bidi-language: AR-SA;" lang="EN-US">Information extraction</span>