Improved Keyword and Keyphrase Extraction from Meeting Transcripts

Sheeba, J.I.; Vivekanandan, K.

doi:10.5120/8260-1800

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…Sementara itu, analisis semantik biasanya menimbulkan ketidakjelasan dan ketidakpastian sehingga pendekatan fuzzy dapat digunakan untuk menyelesaikan masalah tersebut [14]. Dengan mengadopsi fungsi keanggotaan penerapan fuzzy mampu memperbaiki masalah ketidakpastian [15]. Untuk melakukan pencocokan dokumen, dibutuhkan kata atau kalimat acuan untuk mengetahui tingkat keterkaitan antar kata atau kalimat.…”

Section: Pendahuluanunclassified

Kombinasi Ekstraksi Kata Kunci dan Ekspansi Kueri Untuk Deteksi Isu Etik pada Ringkasan Penelitian Kesehatan

Hamidi

Purwitasari²,

Anggraini³

2023

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Penelitian kesehatan harus melalui proses telaah etik yang bertujuan untuk mengantisipasi dugaan atas risiko fisik, sosial, ekonomi dan psikologis. Secara etik penelitian kesehatan dapat diterima apabila pada penelitian tersebut mampu dibuktikan dengan metode ilmiah yang valid serta lulus uji etik sebelum penelitian dilakukan. Untuk memastikan pada ringkasan penelitian kesehatan terdapat aspek etik, dibutuhkan kata kunci yang dapat dijadikan representasi dari isi ringkasan tersebut. Salah satu pendekatan yang sering dilakukan adalah dengan menghitung frekuensi kemunculan kata dalam dokumen. Pendekatan lain yaitu pendekatan YAKE dan keyBERT yang tidak hanya menghitung frekuensi kata namun juga menghitung konteks kata. Selain melakukan ekstraksi dilakukan juga proses ekspansi kueri sebagai upaya memperluas istilah yang dapat mewakili masing-masing aspek etik. Salah satu pendekatan yang digunakan untuk ekspansi kueri adalah model word2Vec. Penelitian ini mengusulkan metode pengembangan ekspansi kueri dengan dan metode ekstraksi kata kunci seperti TFIDF,YAKE dan keyBERT dan mengombinasikannya dengan fuzzy. Hasil eksperimen menunjukkan bahwa metode paling unggul secara presisi yaitu YAKE dan gabungan antara TFIDF + YAKE + keyBERT dengan nilai tertinggi 46% kemudian dari untuk recall model YAKE mendapat nilai tertinggi dengan angka 72% dan untuk nilai F1-Score yang paling unggul adalah metode YAKE dengan nilai tertinggi 54%.

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Section: Pendahuluanunclassified

Kombinasi Ekstraksi Kata Kunci dan Ekspansi Kueri Untuk Deteksi Isu Etik pada Ringkasan Penelitian Kesehatan

Hamidi

Purwitasari²,

Anggraini³

2023

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“…Most of the time, the meeting transcripts are too long, making reading and analyzing the core content infeasible; therefore, providing a framework that can extract the keywords automatically from the meeting transcripts is instrumental. Towards this end, Sheeba and Vivekanandan [224] proposed a model in which the keywords and key phrases are extracted from meeting transcripts. They claimed that the difficulty of this work is tied to the occurrence of synonyms, homonyms, hyponymy, and polysemy in the transcripts.…”

Section: Meeting Transcriptsmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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“…Popular methods include frequency-based unsupervised measures of importance, such as Term Frequency-Inverse Document Frequency (TF-IDF), and word co-occurrence measures (HaCohen-Kerner et al, 2005;Matsuo and Ishizuka, 2004), which are primarily used for extracting relevant keywords from text documents. Other supervised measures of word importance have been proposed (Liu et al, 2011(Liu et al, , 2004Hulth, 2003;Sheeba and Vivekanandan, 2012; for various applications. Closest to our current work, researchers in described a neural network-based model for capturing the importance of a word at the sentence level.…”

Section: Related Workmentioning

confidence: 99%

Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues

Kafle¹,

Alm²,

Huenerfauth³

2019

Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies

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Prosodic cues in conversational speech aid listeners in discerning a message. We investigate whether acoustic cues in spoken dialogue can be used to identify the importance of individual words to the meaning of a conversation turn. Individuals who are Deaf and Hard of Hearing often rely on real-time captions in live meetings. Word error rate, a traditional metric for evaluating automatic speech recognition (ASR), fails to capture that some words are more important for a system to transcribe correctly than others. We present and evaluate neural architectures that use acoustic features for 3-class word importance prediction. Our model performs competitively against state-ofthe-art text-based word-importance prediction models, and it demonstrates particular benefits when operating on imperfect ASR output.

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Improved Keyword and Keyphrase Extraction from Meeting Transcripts

Cited by 9 publications

References 5 publications

Kombinasi Ekstraksi Kata Kunci dan Ekspansi Kueri Untuk Deteksi Isu Etik pada Ringkasan Penelitian Kesehatan

Kombinasi Ekstraksi Kata Kunci dan Ekspansi Kueri Untuk Deteksi Isu Etik pada Ringkasan Penelitian Kesehatan

Text Mining in Big Data Analytics

Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues

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