Study of Different Document Representation Models for Finding Phrase-Based Similarity

Kathiria, Preeti; Arolkar, Harshal

doi:10.1007/978-981-13-1742-2_45

Cited by 3 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The comparative analysis of all these models as per the various factors like time and space complexity to store and retrieve a text, the capability to identify the dynamic length of phrase and data structure used for storing text, and so on, is shared by authors Kathiria and Arolkar in their paper (Kathiria & Arolkar, 2019). Based on time, space complexity, and dynamic length phrase identification, it is concluded that the DIG model generates a graph, matches and identifies phrases in linear time almost.…”

Section: Related Workmentioning

confidence: 99%

Trend analysis and forecasting of publication activities by Indian computer science researchers during the period of 2010–23

Kathiria

Arolkar

2022

Expert Systems

Self Cite

View full text Add to dashboard Cite

Huge collections of published research documents are available in various repositories in Indian universities and research organizations. The efficient retrieval, estimation of research trends, and identification of research gaps with the global trend in different research areas, can be a great guiding tool for regulating research in the appropriate direction. This research attempts to analyse the trend of research activities carried out by Indian researchers in the discipline of Computer Science from 2010 to 19 and forecast the upcoming research trend for the years 2020-23. A repository of the abstracts based on domains given in the Computer Science Ontology (CSO) published by the Indian researchers is developed from the Scopus database. Document Index Graph document representation model is used to store the repository, shared phrases across the documents are extracted and phrase-based similarity is computed. Combining the Single-term and Phrase-based similarity, the hybrid similarity is generated and similar documents are clustered using the DBSCAN clustering technique. Topics are identified for each cluster using the Latent Dirichlet Allocation algorithm and are automatically labelled using CSO. For each topic, the trend analysis and forecasting have been done using the Auto-Regressive Integrated Moving Average. For the assessment of the forecasting performance, the dataset from 2010 to 17 is used as a training dataset and 2018-19 as a testing dataset. The average forecasting for the year 2018 for all CSO domains belongs to the Good forecasting category with Mean Absolute Percentage Error (MAPE) 18.34, and 2019 shows reasonable forecasting with MAPE 30.20 as per the MAPE interpretation given by Lewis. For each topic the average forecasting for years 2018-19 shows either Highly accurate, Good or Reasonable forecasting. As a result, the top four domains for the years 2020-23 are also identified which can help initial researchers in the identification of a relevant topic for research and exploration.

show abstract

Section: Related Workmentioning

confidence: 99%

Trend analysis and forecasting of publication activities by Indian computer science researchers during the period of 2010–23

Kathiria

Arolkar

2022

Expert Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The comparative analysis of the n-gram, Suffix tree and Document Index Graph models are given by the authors P. Kathiria and H. Arolkar [10]. In the paper, different parameters like time complexity for storing and retrieving data, space complexity, data structure to be used to store data, identification of dynamic length of phrase are considered for the analysis.…”

Section: Related Workmentioning

confidence: 99%

Document Clustering based on Phrase and Single Term Similarity using Neo4j

2020

IJITEE

View full text Add to dashboard Cite

Document similarity generally rely on single term similarity such as cosine similarity. To achieve better document similarity, along with single term phrase- more informative feature can be used. To find out shared phrases across the corpus the Document Index graph (DIG) representation model is used. Document representation - DIG model incrementally construct the graph and simultaneously finds the shared phrase between current document and previously inserted documents from the graph. The similarity between documents is mainly depends on the number of shared phrases and single term similarity – known as hybrid similarity. The hybrid similarities are used with wellknown density based clustering technique DBSCAN to assess their effect on quality of the clusters. Experimental results shows that hybrid similarity gives more accurate degree of document similarity and performs better cohesive clustering.

show abstract

Performance Analysis of Document Similarity-Based DBSCAN and K-Means Clustering on Text Datasets

Kathiria

Pandya

Arolkar

et al. 2023

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Study of Different Document Representation Models for Finding Phrase-Based Similarity

Cited by 3 publications

References 9 publications

Trend analysis and forecasting of publication activities by Indian computer science researchers during the period of 2010–23

Trend analysis and forecasting of publication activities by Indian computer science researchers during the period of 2010–23

Document Clustering based on Phrase and Single Term Similarity using Neo4j

Performance Analysis of Document Similarity-Based DBSCAN and K-Means Clustering on Text Datasets

Contact Info

Product

Resources

About