Academic Expert Finding in Indonesia using Word Embedding and Document Embedding: A Case Study of Fasilkom UI

Rampisela, Theresia V.; Yulianti, Evi

doi:10.1109/icoict49345.2020.9166249

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…In addition, since word embedding has dense representation, then it also solves the sparsity problem in the classic representation, which makes the computation more effective and efficient. Therefore, most of the current research in text processing uses word embedding as the word representation [35]- [38]. There are three variants of word embedding that are explored in this work: Word2Vec, FastText, and BERT.…”

Section: Word Embeddingmentioning

confidence: 99%

Enhanced TextRank using weighted word embedding for text summarization

Yulianti,

Pangestu,

Jiwanggi

2023

IJECE

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The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization.

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Section: Word Embeddingmentioning

confidence: 99%

Enhanced TextRank using weighted word embedding for text summarization

Yulianti,

Pangestu,

Jiwanggi

2023

IJECE

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“…These representations were evaluated for their quality in a word similarity task. The Word2Vec model has demonstrated its capability to address various tasks, including text summarization [26,27,28], ranking for academic expert finding [29,30], and text classification [31,32,33]. Several models similar to Word2Vec were subsequently introduced, including Glove (Global Vectors for Word Representation) [34] and FastText [35].…”

Section: A Text Embeddingmentioning

confidence: 99%

Incorporating News Tags into Neural News Recommendation in Indonesian Language

Kahfi,

Yulianti,

Wicaksono

2023

IJACSA

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News recommendation system holds the potential to aid users in discovering articles that align with their interests, which is critical to alleviate user information overload. To generate effective news recommendations, one key capability is to accurately capture the contextual meaning of text in the news articles, since this is pivotal in acquiring useful representations for both news content and users. In this work, we examine the effectiveness of neural news recommendation with attentive multi-view learning (NAML) method to conduct a news recommendation task in the Indonesian language. We further propose to incorporate news tags, which at some levels may capture the important contextual meanings contained in the news articles, to improve the effectiveness of the NAML method in the Indonesian news recommendation system. Our results show that the NAML method leads to significant improvement (if not comparable) in the effectiveness of neural-based Indonesian news recommendations. Further incorporating news tags is shown to significantly increase the performance of the NAML method by 5.86% in terms of NDCG@5 metric.

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“…Different retrieval methods, such as: probabilistic BM25 [30], [31] and sequential dependence model (SDM) [32], [33] can be considered for further study. The possibility of combining semantic information [25], [34], [35] or social media [36], [37] into the retrieval model to address lexical mismatch problem is a great challenge to be explored.…”

Section: Analysis Of Subject Headings Overlaps Of Documents From Different Facultiesmentioning

confidence: 99%

Determining subject headings of documents using information retrieval models

Yulianti

Rahadianti²

2021

IJEECS

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Subject heading is a controlled vocabulary that describes the topic of adocument, which is important to find and organize library resources. Assigning appropriate subject headings to a document, however, is a time-consuming process. We therefore conduct a novel study on the effectiveness of information retrieval models, i.e.,language model (LM) andvector spacemodel (VSM), to automatically generate a ranked list of relevant subject headings, with the aim to give a recommendation for librarians to determine the subject headings effectively and efficiently. Our results show that there are a high number of our queries (up to 61%) that have relevant subject headings in the ten top-ranked recommendations and on average, the first relevant subject heading is found at the early position (3rd rank). This indicates that document retrieval methods can help the subject heading assignment process. LM and VSM are shown to have comparable performance, except when the search unit is title, VSM is superior to LM by8-22%. Our further analysis exhibits three faculty pairs that are potential to have research collaboration as their students’ thesis often have overlap subject headings: i) economy and business-social and political sciences, ii) nursing-public health and iii) medicine-public health.

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Academic Expert Finding in Indonesia using Word Embedding and Document Embedding: A Case Study of Fasilkom UI

Cited by 10 publications

References 16 publications

Enhanced TextRank using weighted word embedding for text summarization

Enhanced TextRank using weighted word embedding for text summarization

Incorporating News Tags into Neural News Recommendation in Indonesian Language

Determining subject headings of documents using information retrieval models

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