NusaCrowd: Open Source Initiative for Indonesian NLP Resources

Cahyawijaya, Samuel; Lovenia, Holy; Aji, Alham Fikri; Winata, Genta Indra; Wilie, Bryan; Mahendra, Rahmad; Wibisono, Christian; Romadhony, Ade; Vincentio, Karissa; Koto, Fajri; Santoso, Jennifer; Moeljadi, David; Wirawan, Cahya; Hudi, Frederikus; Parmonangan, Ivan Halim; Alfina, Ika; Wicaksono, Muhammad Satrio; Putra, Ilham Firdausi; Samsul, Rahmadani,; Yulianti, Oenang,; Septiandri, Ali Akbar; James, Jaya,; Dhole, Kaustubh; Suryani, Arie Ardiyanti; Putri, Rifki Afina; Su, Dan; Stevens, Keith; Nityasya, Made Nindyatama; Adilazuarda, Muhammad; Ignatius, Ryan; Diandaru, Ryandito; Yu, Tiezheng; Vito, Ghifari,; Dai, Wujiao; Xu, Yan; Dyah, Damapuspita,; Tho, Cuk; Karo, Ichwanul Muslim Karo; Fatyanosa, Tirana Noor; Ji, Ziwei; Fung, Pascale; Neubig, Graham; Baldwin, Timothy; Ruder, Sebastian; Sujaini, Herry; Sakti, Sakriani; Purwarianti, Ayu

doi:10.48550/arxiv.2212.09648

Cited by 3 publications

(5 citation statements)

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“…Table 9 shows that the results of the comparison between summarization and manual summaries have an average recall value of 61%, precision of 88%, and f-measure of 70%. Table 10 shows the comparison between the MMR summary result and another model [41]. As seen in Table 10, the Bert2-GPT-Id has a shorter summary than MMR.…”

Section: Results and Experimentsmentioning

confidence: 99%

“…The summary evaluation is measured by comparing the manual and automated summaries [41]. Manual summaries were obtained from manual summaries of 20 respondents and calculated with precision, recall, and f-measure values as in ( 6) to (8).…”

Section: D6mentioning

confidence: 99%

“…A collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources [18]. The paper describes the datasets and standardized data loaders that were brought together through this initiative and discusses the quality of the datasets, which were assessed manually and automatically.…”

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confidence: 99%

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Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Gunawan,

Fitria,

Setiawan

et al. 2023

Knowl. Eng. Data Sci.

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Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of the entire contents of the article. Which then presents it back in summary form. The steps in this research will start with the user selecting or searching for text documents that will be summarized with keywords in the abstract as a query. The proposed approach performs text preprocessing for documents: sentence breaking, case folding, word tokenizing, filtering, and stemming. The results of the preprocessed text are weighted by term frequency-inverse document frequency (tf-idf), then weighted for query relevance using the vector space model and sentence similarity using cosine similarity. The next stage is maximum marginal relevance for sentence extraction. The proposed approach provides comprehensive summarization compared with another approach. The test results are compared with manual summaries, which produce an average precision of 88%, recall of 61%, and f-measure of 70%.

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Section: Results and Experimentsmentioning

confidence: 99%

Section: D6mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Gunawan,

Fitria,

Setiawan

et al. 2023

Knowl. Eng. Data Sci.

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show abstract

“…Indonesia has one of the largest populations in the world (Cahyawijaya et al, 2021), yet the availability of resources and the progress of NLP research in Indonesian lag behind (Wilie et al, 2020;Cahyawijaya et al, 2022). Nevertheless, research on Indonesian, both speech and text, is gradually progressing despite the challenges mentioned earlier.…”

Section: Related Workmentioning

confidence: 99%

“…Even though the majority of research and existing data in Indonesian comes from the text modality, several works have attempted some speech processing tasks in Indonesian. To date, some speech corpora have also been developed (Sakti et al, 2004;Lestari et al, 2006;Sakti et al, 2008aSakti et al, , 2013, as well as the development of ASR (Sakti et al, 2004(Sakti et al, , 2013Ferdiansyah and Purwarianti, 2011;Prakoso et al, 2016;Cahyawijaya et al, 2022) and TTS (Sakti et al, 2008b;Azis et al, 2011;Mengko and Ayuningtyas, 2013;Azizah et al, 2020). Most Indonesian ASRs and TTSs have achieved good results.…”

Section: Related Workmentioning

confidence: 99%

Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian

Widiaputri,

Purwarianti,

Lestari

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Classification of SWOT Statements Employing BERT Pre-Trained Model Embedding

Thamrin,

Oktafiani,

Rasyid

et al. 2024

JSinbis

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SWOT analysis is a highly effective method for organizations to develop strategic planning and gain widespread adoption by various institutions, industries, and businesses. The importance of SWOT analysis lies in its ability to provide a comprehensive assessment of an organization's internal and external factors. Despite its advantages, there are several challenges in its implementation, such as the challenge to identify the four elements of SWOT and to put statements into their correct position as strength, weakness, opportunity, or threat. This study aims to determine the best SWOT statement classification from a combination of using BERT models as feature extraction technique and compare it with traditional method of TF-IDF. The SWOT statement is input to the model to get a vector as a sentence representation. More similar vector representations indicate the closer meaning of the sentences. The similarity is the basis for the classifier to determine whether a sentence falls into the domain S, W, O, or T. We examined two classification algorithms, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). Data consists of 635 SWOT statements from study programs of a higher education institution. Five combinations of feature extraction techniques and classification algorithms were tested. The study finds that SBERT model embedding in conjunction with support vector machine classification yield the best performance with an accuracy of 0.73 and an F1-score of 0.738. It outperforms the more traditional method of feature extraction of TF-IDF and other combinations using the Naive Bayes Classifier.

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NusaCrowd: Open Source Initiative for Indonesian NLP Resources

Cited by 3 publications

References 0 publications

Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Speech Recognition and Meaning Interpretation: Towards Disambiguation of Structurally Ambiguous Spoken Utterances in Indonesian

Classification of SWOT Statements Employing BERT Pre-Trained Model Embedding

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