Pendekatan Teknik Pengecaman Entiti Nama Bagi Capaian Berita Jenayah Bahasa Melayu (Named Entity Recognition Approach for Malay Crime News Retrieval)

Saidah Saad, Mohamed Kamil Mansor

Abstract


Pengekstrakan maklumat merupakan satu proses bagi mendapatkan konsep penting dalam mewakili kandungan teks dari dokumen yang tidak berstruktur. Pada masa kini, terdapat banyak dokumen yang tidak berstruktur seperti teks berita, artikel blog, forum, tweet serta mikro blog dari rangkaian sosial. Dokumen-dokumen ini amat sukar untuk difahami oleh komputer. Oleh itu, kajian berkaitan pengekstrakan maklumat menjadi sangat penting bagi mengatasi permasalah ini. Salah satu teknik pengekstrakan yang banyak digunakan ialah pengecaman entiti nama. Kajian ini dijalankan bagi mengimplementasikan teknik pengecaman entiti nama dari sumber dokumen berita jenayah bahasa Melayu. Objektif utama kajian ini adalah untuk membangunkan sistem prototaip model pengekstrakan maklumat berita jenayah dalam bahasa Melayu dengan menggunakan teknik pengecaman entiti nama melalui pendekatan berasaskan peraturan. Kajian ini dilakukan dengan mewujudkan korpus berita jenayah dalam bahasa Melayu yang diperolehi dari sumber arkib berita BERNAMA. Korpus ini kemudiannya diteliti secara manual oleh pakar bahasa bagi mengecam entiti nama seperti individu, organisasi, lokasi, tarikh, masa, kewangan, peratusan, jenayah dan senjata. Dalam masa yang sama, sistem prototaip dibangunkan serta diuji dengan korpus yang sama dan hasil dari pengujian ini dibandingkan dengan keputusan pakar. Secara keseluruhannya, ujian sistem prototaip ini menunjukkan hasil yang baik dengan nilai dapatan bagi recall sebanyak 78.67%, manakala bagi precision ialah sebanyak 71.11% dan F-measure sebanyak 74.7%. Hasil dari kajian ini diharap dapat menyumbang kepada pengetahuan mengenai keberkesanan teknik pengecaman entiti nama bagi berita jenayah bahasa Melayu dan seterusnya dapat membantu para penyelidik, polis, peguam serta pihak berkuasa yang terlibat dalam bidang jenayah menyelesaikan jenayah dengan lebih cepat dan berkesan. 

 

Kata kunci: pengekstrakan maklumat; pengecaman entiti nama; Bahasa Melayu, berita jenayah, pendekatan berasaskan peraturan.

 

Abstract

 

Information extraction is a process of obtaining an important concept in representing the textual content of unstructured documents. At present, there are a lot of unstructured documents such as news, articles, blogs, forums, tweets and micro-blogs of social networks. These documents are very difficult to be understood by the computer. Therefore, studies on the extraction of information is very important to overcome  this problem. One extraction technique that is widely used is the entity name recognition. This research aims to implement the entity name recognition techniques of crime news source document in Malay language. The main objective of this study is to develop a prototype system model information extraction crime news in the Malay language using name entity recognition through a rule-based approach. This assessment is done by creating a corpus of crime news in the Malay language which is derived from the archival source; BERNAMA news. The corpus is then examined manually by linguists to identify individual entities such as name, organization, location, date, time, financial, percentage, crime and weapons. At the same time, a prototype system was developed and tested with the same corpus and the results of these tests were compared with the results of an expert. Overall, these tests showed good results with the findings for the recall at 78.67%, while precision is at 71.11% and for F-measure at 74.7%. The results of this study are expected to contribute knowledge regarding the effectiveness of the entity's name recognition techniques for crime news Malay language. This  could further assist investigators, police, lawyers and authorities involved in the field of crime in solving crimes more quickly and effectively.

 

Keywords: information extraction; named entity recognition; malay language, crime news, rule-based approach


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References


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DOI: http://dx.doi.org/10.17576/gema-2018-1804-14

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