Design and Develop Semantic Textual Document Clustering Model

Fahad, SK Ahammad

doi:10.15640/jcsit.v5n2a4

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…The model, tested with movie reviews and product reviews (books, DVD, electronic and kitchen) was compared with seven other methods of sentiment classification and results showed the model outperformed all other methods in all cases. Also, [9] used semantic clustering to locate and access web documents. The text corpus is pre-processed, stemming is performed using the WordNet ontology.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantics-based clustering approach for similar research area detection

et al. 2020

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The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results.

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Section: Literature Reviewmentioning

confidence: 99%

Semantics-based clustering approach for similar research area detection

et al. 2020

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“…Second, recommender systems that exploit hierarchical organizations can be developed [32]. Third, clustering stories together into subtypes based on themes they share in common is another interesting application [33]. And fourth, a method could be developed to identify themes whose usages differ significantly between two group of stories over time (e.g.…”

Section: Lto Applicationsmentioning

confidence: 99%

The Literary Theme Ontology for Media Annotation and Information Retrieval

Sheridan,

Onsjö,

Hastings

2019

Preprint

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Literary theme identification and interpretation is a focal point of literary studies scholarship. Classical forms of literary scholarship, such as close reading, have flourished with scarcely any need for commonly defined literary themes. However, the rise in popularity of collaborative and algorithmic analyses of literary themes in works of fiction, together with a requirement for computational searching and indexing facilities for large corpora, creates the need for a collection of shared literary themes to ensure common terminology and definitions. To address this need, we here introduce a first draft of the Literary Theme Ontology. Inspired by a traditional framing from literary theory, the ontology comprises literary themes drawn from the authors own analyses, reference books, and online sources. The ontology is available at https://github.com/theme-ontology/lto under a Creative Commons Attribution 4.0 International license (CC BY 4.0).

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“…The standard approach is single-word tokenization, in which the input string is split word by word using spaces as separators [9]. Most NLP research uses this kind of tokenization technique, such as by [10] in semantic similarity, [4] [9] in text classification, [11], [12] in information retrieval, [13], [14] in clustering, [15]- [17] in sentiment analysis, and much more.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach: Tokenization Framework based on Sentence Structure in Indonesian Language

Petrus¹,

Ermatita²,

Sukemi³

et al. 2023

IJACSA

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This study proposes a new approach in the sentence tokenization process. Sentence tokenization, which is known so far, is the process of breaking sentences based on spaces as separators. Space-based sentence tokenization only generates single word tokens. In sentences consisting of five words, tokenization will produce five tokens, one word each. Each word is a token. This process ignores the loss of the original meaning of the separated words. Our proposed tokenization framework can generate one-word tokens and multi-word tokens at the same time. The process is carried out by extracting the sentence structure to obtain sentence elements. Each sentence element is a token. There are five sentence elements that is Subject, Predicate, Object, Complement and Adverbs. We extract sentence structures using deep learning methods, where models are built by training the datasets that have been prepared before. The training results are quite good with an F1 score of 0.7 and it is still possible to improve. Sentence similarity is the topic for measuring the performance of one-word tokens compared to multi-word tokens. In this case the multiword token has better accuracy. This framework was created using the Indonesian language but can also use other languages with dataset adjustments.

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Design and Develop Semantic Textual Document Clustering Model

Cited by 8 publications

References 27 publications

Semantics-based clustering approach for similar research area detection

Semantics-based clustering approach for similar research area detection

The Literary Theme Ontology for Media Annotation and Information Retrieval

A Novel Approach: Tokenization Framework based on Sentence Structure in Indonesian Language

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