Effective data curation for frequently asked questions

Miyamoto, Kohtaroh; Koseki, Akira; Ohno, Masaki

doi:10.1109/soli.2017.8120960

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…Kumpulan pertanyaan dan jawaban pada FAQ umumnya disediakan pada sebuah halaman yang dapat di akses oleh pengguna. Pengguna secara aktif mencari pertanyaan yang sesuai dengan kondisi dan menemukan jawaban nya, hal ini akan menjadi kendala apabila daftar pertanyaan cukup banyak sehingga membutuhkan waktu untuk mencari pertanyaan yang relevan dengan kebutuhan [2]. Untuk mengatasi hal ini diperlukan sebuah sistem yang dapat menyediakan information retrieval berdasarkan input dari pengguna [3].…”

Section: Pendahuluanunclassified

Information Retrieval Pada Frequently Asked Questions (FAQ) dengan metode String Similarity

Setiawan

Adnyana

2022

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Information retrieval merupakan sebuah sarana untuk menemukan informasi berdasarkan kumpulan informasi pada data terstruktur maupun tidak terstruktur secara otomatis. implementasi information retrival seperti mesin pencari menggunakan query dari pengguna dengan bahasa alami manusia kemudian sistem dapat menemukan dokumen atau informasi yang berkaitan dengan query dari pengguna. Pada penelitian ini di usulkan sistem information retrieval pada Frequently Asked Questions atau FAQ dengan mencari pertanyaan yang mirip (similar) pada daftar pertanyaan di basis data terhadap pertanyaan yang diberikan oleh pengguna menggunakan algoritma Cosine similarity untuk mencari kesamaan kosinus tertinggi. Selanjutnya memberikan respon jawaban yang sebelum nya sudah di berikan label terhadap pertanyaan yang relevan dan memiliki similaritas paling tinggi. Telah dihasilkan dataset FAQ dan dilakukan preprocessing, penerapan algoritma Cosine Similarity terhadap input pertanyaan (query) dengan dataset dan menghasilkan bobot pada setiap pertanyaan (label) pada dataset. Melalui evaluasi akurasi pemberian bobot similaritas yang dilakukan dengan memberikan sembilan input pertanyaan dibagai pada tiga kategori berdasarkan tingkat kemiripan memiliki akurasi mencapai 100%, dengan demikian information retrieval dengan Cosine similarity telah mampu memberikan bobot sesuai dengan tingkat similaritas pertanyaan (query) dengan dataset pertanyaan pada FAQ

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Section: Pendahuluanunclassified

Information Retrieval Pada Frequently Asked Questions (FAQ) dengan metode String Similarity

Setiawan

Adnyana

2022

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“…Natural Language Processing Algorithms nowadays are becoming more popular and valuable in textual data processing and data curation combined with this sort of algorithms may yield valuable insights for the data being on the motion. Miyamoto et al (2017), introduce a system for enhancing Question-Answering automation based on NLP and data curation that is intended for detecting the language, validating the desired pairs of the questions and answers based on predefined patterned-based topic segmentation for enhanced next levels of data processing.…”

Section: Natural Language Processingmentioning

confidence: 99%

Data Streams Curation for Better Machine Learning Functionality and Result to Serve IoT and other Applications: A Survey

Salah¹,

Al-Omari²,

Alwidian³

et al. 2019

Journal of Computer Science

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Data Curation on data streams is effective in operating and reducing costs of BIG DATA analytic. Basically, analytic preparation requires data curation of available heterogeneous data sets available in big data clusters and such analytic process becomes harder when it comes to the concept of conducting the curation process on Data-on-Motion, in order to come at actionable insights and valuable analytic on a real-time basis including the Machine Learning further analytic and processing. In our paper, we identified and surveyed the different issues and challenges among different areas that are related to the big data. In addition to investigate, the most common techniques and methods followed through the implementations including Streams Curation, the Machine Learning Different Algorithms used in such implementations and the Feature Engineering different techniques that can be considered as curation pre-processing paradigm for data streams analytic. Furthermore, our paper shows the different application areas were data curation concept plays a critical role. Finally, we draw the map between the techniques and methods that are related to the data curation field to emphasize on its main critical role among Business, Retails, Culture, Arts, Health, Medicine, Social Media, Wireless Sensor Networks, Natural Language Processing (NLP) and Automated Feature Engineering (FE). On other hand, we identified the different issues and challenges among different areas including the IoT and Media Streams Curation to help the scholars in this region accordingly.

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Text Classification of Facebook Messages Using Multiclass Support Vector Machine

Ebora

Espanol

Padilla

2022

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Effective data curation for frequently asked questions

Cited by 3 publications

References 13 publications

Information Retrieval Pada Frequently Asked Questions (FAQ) dengan metode String Similarity

Information Retrieval Pada Frequently Asked Questions (FAQ) dengan metode String Similarity

Data Streams Curation for Better Machine Learning Functionality and Result to Serve IoT and other Applications: A Survey

Text Classification of Facebook Messages Using Multiclass Support Vector Machine

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