Single document keywords extraction in Bahasa Indonesia using phrase chunking

Trisna, I Nyoman Prayana; Nurwidyantoro, Arif

doi:10.12928/telkomnika.v18i4.14389

Cited by 4 publications

(4 citation statements)

References 8 publications

(20 reference statements)

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“…KE can be used for several purposes such as categorizing documents into specific groups to make them easier to find, summarizing one or several documents into fewer sentences without losing the core idea of the document, as well as searching for semantic similarity of content in multiple documents. [2]. This can also imply that KE is one of the most important initial phases in processing text data.…”

Section: Keyword Extraction Keyphrase Extractionmentioning

confidence: 98%

“…If N is more than 1, then the longer phrase has a higher weight than the lower phrase, so the phrase "government regulation" has a higher weight than the individual words "regulation" and "government" separately. The equation for N-Gram generation is shown in (2).…”

Section: Yet Another Keyword Extractor (Yake)mentioning

confidence: 99%

“…Keyword or keyphrase extraction (KE) is an important process to obtain main information contained in one or several documents. Many studies on keyword extraction were conducted either using supervised approach (based on machine learning) such as [1] or unsupervised approach (based on statistics and ranking) as in [2]. One of the KE methods that uses an unsupervised approach is YAKE [3] which has the advantage of being able to perform KE on a document independently, which means it does not require a corpus to form language tools (such as NER and POS-tag).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Keyphrase Extraction on Covid-19 Tweets Based on Doc2Vec and YAKE

Firdausillah¹,

Udayanti²

2021

JAIS

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Keyword and keyphrase extraction are one of the initial foundations for performing several text processing operations such as summarization and document clustering. YAKE is one of the techniques used for unsupervised and independent keyphrase extraction, it does not require a corpus for linguistic tools such as NER and POS-tag. However, the use of YAKE in microblogging documents such as Twitter often results in a keyphrase that is less representative because of the lack of words used for ranking. This paper offers a solution to this problem by looking for similar tweets in the keyphrase extraction process using Doc2Vec so that the number of words used in the YAKE ranking process can be greater. Covid-19 tweets related are used as dataset as the topic is currently widely discussed on social media to prove that the proposed approach could improve keyphrase extraction performance

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Section: Keyword Extraction Keyphrase Extractionmentioning

confidence: 98%

Section: Yet Another Keyword Extractor (Yake)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Keyphrase Extraction on Covid-19 Tweets Based on Doc2Vec and YAKE

Firdausillah¹,

Udayanti²

2021

JAIS

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“…POS can provide some information about a word (noun, verb) and the words around it (possessive pronoun, personal pronoun) in natural language processing [19]. POS tag can mark a term that has appeared more often in the document and can be the most important term [20]. Based on Jespersen's Rank Theory, POS can be ranked into four degrees: i) nouns, because they have the most content-bearing labels, ii) adjectives, verbs, and participles, ii) adverbs, and finally iv) all remaining POS [13].…”

Section: Grammatical Informationmentioning

confidence: 99%

Feature-based POS tagging and sentence relevance for news multi-document summarization in Bahasa Indonesia

Abdullah

Fatichah

2022

Bulletin EEI

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Sentence extraction in news document summarization determines representative sentences primarily by employing the news feature known as news feature score (NeFS). NeFS can achieve meaningful sentences by analyzing the frequency and similarity of phrases while neglecting grammatical information and sentence relevance to the title. The presence of instructive content is indicated by grammatical information carried by part of speech (POS). POS tagging is the process of giving a meaningful tag to each term based on qualified data and even surrounding words. Sentence relevance to the title is intended to determine the sentence's level of connectivity to the title in terms of both word-based and meaning-based similarity, primarily for news documents in Bahasa Indonesia. In this study, we present an alternative sentence weighting method by incorporating news features, POS tagging, and sentence relevance to the title. Sentence extraction based on news features, POS tagging, and sentence relevance is introduced to extract the representative sentences. The experiment results on the 11 groups of Indonesian news documents are compared with the news features scores with the grammatical information approach method (NeFGIS). The proposed method achieved better results. The increasing f-score rate of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 sequentially are 1.84%, 3.03%, 3.85%, 2.08%.

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Document Searching of EPPS Test Result Using Indexing Method

Krisna

Mulyati

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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This study focuses on documents searching on the EPPS test result, mainly administered in the education sector for selecting prospective students in universities’ admissions process. There has been a considerable increase of admission data along with annually administered admission test for prospective students in every beginning of an academic year. Some faculties may administer the EPPS test in the second stage test of admission process after the prospective students pass the first stage in addition to having the general admission test. These several tests lead to a huge data accumulation with different information, making it difficult for the organizers to locate the document containing 15 aspects of required information in a comprehensive information display. Thus, it is necessary to apply an indexing method by having a predetermined index to retrieve required information in a particular document or to find other related documents by way of indexing method. This research used 74 pdf documents as the dataset. In particular, 60 datasets were used as references to retrieve phrases on the specified data, and 14 datasets were used to test data. The results revealed the most significant data with an average precision of 0.89, recall of 0.93 and f-measureof 0.91.

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Single document keywords extraction in Bahasa Indonesia using phrase chunking

Cited by 4 publications

References 8 publications

Keyphrase Extraction on Covid-19 Tweets Based on Doc2Vec and YAKE

Keyphrase Extraction on Covid-19 Tweets Based on Doc2Vec and YAKE

Feature-based POS tagging and sentence relevance for news multi-document summarization in Bahasa Indonesia

Document Searching of EPPS Test Result Using Indexing Method

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