Indonesian Language Term Extraction using Multi-Task Neural Network

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two multilayer perceptron neural networks to perform Part-of-Speech (PoS) labeling and Noun Phrase Chunking. Our models were trained as a joint model to solve this problem. Our proposed method, with an f-score of 86.80%, can be considered a state-of-the-art algorithm for performing term extraction in the Indonesian Language using noun phrase chunking

Pengembangan Mobile based Question Answering System dengan Basis Pengetahuan Ontologi

Informasi terkait kegiatan penerimaan mahasiswa baru (PMB) sesungguhnya telah banyak tersedia pada halaman web maupun brosur. Namun demikian, dimungkinkan terdapat berbagai informasi yang tidak dapat ditemukan secara langsung dalam media tersebut. Penggunaan mesin pencari juga tidak menjamin pengguna untuk mendapatkan informasi atau jawaban yang relevan dengan kebutuhan. Melakukan kunjungan ke kampus seringkali terkendala oleh jarak, waktu, dan jam kerja. Dalam penelitian ini, dikembangkan sebuah question answering system (QAS) terkait penerimaan mahasiswa baru agar pengguna mendapakan informasi yang sesuai dengan kebutuhannya, selalu bernilai benar, dan dapat diakses kapan saja. QAS dibangun dengan arsitektur tree tier dengan aplikasi mobile sebagai antarmuka, memanfaatkan metode pengolahan bahasa alami dalam memproses pertanyaan pengguna, dan ontologi sebagai basis pengetahuannya. Penelitian ini menggunakan model pengembangan SDLC, dengan model analisis yang digunakan yaitu: analisis kebutuhan sistem, analisis rancangan sistem, implementasi sistem, dan pengujian sistem. Pengujian terhadap sistem dilakukan dengan beberapa cara, yaitu: usability testing, dan pengujian akurasi jawaban. Pengujian menunjukkan QAS yang dibangun dapat diimplementasikan dengan baik sesuai dengan kebutuhan dengan akurasi jawaban sebesar 82.14%. AbstractThe information regarding student admissions and related activities can be found and widely available on website or brochures. However, it is possible that the relevant information cannot be found directly from the media. The use of search engines also doesn’t guarantee users to get the relevant answer or information that satisfy their needs. Visiting the campus is often constrained by distance, time or working hours. In this study, a question answering system related to student admissions was developed so that users get the information that fits thier need, always give the correct answers, and can be accessed anytime. The QAS is built with a tree tier architecture with a mobile application as an interface. Natural language processing methods uses to process user questions, and ontology uses as the knowledge base. This study uses the SDLC development model, with the analysis model used namely: system requirements analysis, system design analysis, system implementation, and system testing. Testing the system is done by several ways, namely: usability testing, and test the accuracy of answers. The tests shows that the QAS can successfully implemented according to the requirement, with the accuracy of answer is 82.14%

Improving Retrieval of Information from the Internet

To improve the quality of the search result returned by the internet which makes users have to look through a huge amount of links for the real answers, we utilized the high quality links Google produces and the Information Retrieval technology to implement a Question Answering (QA) system. This system analyzes and downloads the text contents from the relevant web pages Google searches based on the users\u27 questions to build a dynamic knowledge collection; retrieves the relevant passages from the collection and sends the ranked passages back. The users can further refine their questions in the query refinement step for the better answers. A novel search strategy was designed to detect the semantic connections between the question and the documents. This answer retrieval also involves the TF-IDF algorithm and Vector Space Model for the document indexing. We have modified the original Cosine Coefficient Similarity Measurement to rank the candidate answers