Information retrieval system in text-based skripsi document search file using vector space model method

Azis, Mochammad Abdul; Hamid, A Toliyat; Fauzi, Ahmad; Yudhistira, Yudhistira; Yulianto, Eko; Riyanto, Verry; Ridwansyah, _; Sfenrianto, Sfenrianto

doi:10.1088/1742-6596/1367/1/012016

Cited by 5 publications

(4 citation statements)

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“…The Vector Space Model (VSM), a similarity-based algebraic model, represents documents and user queries as a multi-dimensional vector space [1,2]. In VSM [14,28], document (D) is denoted as a dimension vector (D) that consists of term weights to determine the placement of a document in the vector space.…”

Section: Vector Space Modelmentioning

confidence: 99%

“…In this work, we adapt the Bidirectional Encoder Representations from Transformers (BERT) model to represent our research dataspace that mainly consists of research articles as a collection of multi-dimensional vector space [7,35]. The vector space model (VSM) represents the content of both documents and users' queries using n − dimensional vector space [1,2,28]. The VSM model addresses neither the representation of both semantic and linguistic features of documents nor contextual relationships among the words in a sentence; it only focuses on representing the contents of documents.…”

Section: Preliminariesmentioning

confidence: 99%

“…2 end for Compute set D containing the k-smallest distances d(X i , x). return domain label(Y i ) where i ∈ D…”

mentioning

confidence: 99%

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CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

Singh

Pandey²,

Saxena³

et al. 2022

Preprint

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Dataspace systems cope with the problem of integrating a variety of data based on its structures and semantics such as structured, semi-structured, and unstructured data, and returns the best-effort or approximate answers to their users. The existing works on query answering in a dataspace system are content-based and paid attention to return the best answers to the users without taking care of their preferences. This paper aims to consider not only the content-based information but also the users' preferences while answering the users' queries. Therefore, we present a Collaborative Query-Answering Framework for a Research Article Dataspace (CQFaRAD) that helps to efficiently answer the users' queries and returns more prominent answers to them. In this work, we present a collaborative approach that adopts the advantages of existing content-based and users' preferences-based approaches. To achieve this task, we use the BERT model to represent our dataspace and users' query. We have validated our proposed approach on the research papers dataset available on Kaggle. The experimental results show that our approach works fairly well to return relevant information to the users.

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Section: Vector Space Modelmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

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CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

Singh

Pandey²,

Saxena³

et al. 2022

Preprint

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show abstract

“…Moreover, many investigations are focused on solving the aforementioned challenges for different modalities including video [18], [21], [51], text [3] and audio [35], [50]. Most of the investigations mentioned above are based on performing…”

Section: Introductionmentioning

confidence: 99%

A Multimodal End-to-End Deep Learning Architecture for Music Popularity Prediction

et al. 2020

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The continuous evolution of multimedia applications is fostering applied research in order to dynamically enhance the services provided by platforms such as Spotify, Lastfm, or Billboard. Thus, innovative methods for retrieving specific information from large volumes of data related with music arises as a potential challenge within the Music Information Retrieval (MIR) framework. Moreover, despite the existence of several musical-based datasets, there is still a lack of information to properly assess an accurate estimation of the impact or the popularity of a song within a platform. Furthermore, the aforementioned platforms measure the popularity in various manners, thus increasing the difficulties in performing generalized and comparable models. In this paper, the creation of SpotGenTrack Popularity Dataset (SPD) is presented as an alternative solution to existing datasets that will facilitate researchers when comparing and promoting their models. In addition, an innovative multimodal end-to-end Deep Learning architecture named as HitMusicNet is presented for predicting popularity in music recordings. Experiments conducted show that the proposed architecture outperforms previous studies in the State-of-the-Art by incorporating three main modalities to the analysis, such as audio, lyrics and meta-data as well as a preliminary compression stage via autoencoder to better the capability of the model when predicting the popularity.

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CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

Singh,

Pandey,

Saxena

et al. 2023

Int. j. inf. tecnol.

View full text Add to dashboard Cite

Dataspace systems cope with the problem of integrating a variety of data based on its structures and semantics such as structured, semi-structured, and unstructured data, and returns the best-effort or approximate answers to their users. The existing works on query answering in a dataspace system are contentbased and paid attention to return the best answers to the users without taking care of their preferences. This paper aims to consider not only the content-based information but also the users' preferences while answering the users' queries. Therefore, we present a Collaborative Query-Answering Framework for a Research Article Dataspace (CQFaRAD) that helps to efficiently answer the users' queries and returns more prominent answers to them. In this work, we present a collaborative approach that adopts the advantages of existing content-based and users' preferences-based approaches. To achieve this task, we use the BERT model to represent our dataspace and users' query. We have validated our proposed approach on the research papers dataset available on Kaggle. The experimental results show that our approach works fairly well to return relevant information to the users.

show abstract

Information retrieval system in text-based skripsi document search file using vector space model method

Cited by 5 publications

References 1 publication

CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

A Multimodal End-to-End Deep Learning Architecture for Music Popularity Prediction

CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

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