Mining unstructured data for a competitive intelligence system XEW

Haddadi, Amine El; Fennan, Abdelhadi; Haddadi, Anass El; Boulouard, Zakaria; Koutti, Lahcen

doi:10.1109/isei.2015.7358737

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…To overcome it background information about the healthcare domain is used at the time of stop word removal. The stop word lists are used to eliminate the irrelevant words [20]. The term weightage is approximated using the product of term frequency and inverse document frequency.…”

Section: Proposed Systemmentioning

confidence: 99%

Privacy Preserving Text Document Summarization

Shree¹,

Kiran²

2022

J. Engg. Res. & Sci.

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Data Anonymization provides privacy preservation of the data such that input data containing sensitive information is converted into anonymized data. Hence, nobody can identify the information either directly or indirectly. During the analysis of each text document, the unique attributes reveal the identity of an entity and its private data. The proposed system preserves the sensitive data related to an entity available in text documents by anonymizing the sensitive documents either entirely or partially based on the sensitivity context which is very specific to a domain. The documents are categorized based on sensitivity context as sensitive and not-sensitive documents and further, these documents are subjected to Summarization. The proposed Privacy Preserving Text Document Summarization generates crisp privacy preserved summary of the input text document which consists of the most relevant domain-specific information related to the text document without defying an entity privacy constraints with the compression rate of 11%, the precision of 86.32%, and the recall of 84.28%.

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Section: Proposed Systemmentioning

confidence: 99%

Privacy Preserving Text Document Summarization

Shree¹,

Kiran²

2022

J. Engg. Res. & Sci.

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“…In the current era, the amount of information is increasing very rapidly [1], a lot of information is available in text form from various types of documents such as magazines, e-books, research results, social media, emails, pdf files, video, audio, images, and large amounts of business content. Experts predict the volume of text documents will grow by 80% by 2025.…”

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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