Deep Text Mining for Automatic Keyphrase Extraction from Text Documents

Abulaish, Muhammad; Jahiruddin,; Dey, Lipika

doi:10.1515/jisys.2011.017

Cited by 9 publications

(4 citation statements)

References 10 publications

(10 reference statements)

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“…It includes discourse analysis, lexical analysis, and syntactical analysis. The primary resources used for lexical analysis include electronic dictionaries, tree taggers, Wordnet, n-grams, and POS patterns [33]. The syntactical analysis makes use of noun phrases and noun chunks.…”

Section: Keyword Extractionmentioning

confidence: 99%

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

Sapana Kolambe

2023

IJRITCC

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In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches.

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Section: Keyword Extractionmentioning

confidence: 99%

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

Sapana Kolambe

2023

IJRITCC

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“…After ranking candidate term, we take top-n key terms for feature vector generation process. In [26], we had presented details of our key terms (aka key phrases) identification process.…”

Section: B Feature Vector Generationmentioning

confidence: 99%

Content-Based Tweets Clustering and Analysis

Jahiruddin¹

2018

IJCSE

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Abstract-Event-based social network analysis is an important task for monitoring the potential threats to the security of a nation and identifying various trends that are popular among the people. In this paper, we propose content-based tweets clustering and analysis method, which aims to cluster tweets based on the events represented by them. The proposed method starts with modeling tweets into a similarity graph (aka social network), in which each node represent a tweet and an edge connecting a node-pair represents the degree of similarity between the tweets represented by them. For social graph generation, each node is represented as a feature vector which is generated using Latent Dirichlet Allocation (LDA) from the respective tweet and edge weight is determined as the similarity between the nodes. Finally, the generated social graph is partitioned into a number of clusters (sub-graphs) using Markov Clustering (MCL) algorithm, where each sub-graph represent an event. We have generated a data set of 5000 tweets related to four different events -Uri attacks, Delhi assembly election, Union budget 2015, and Israel-Gaza conflict to evaluate the proposed method. The experimental results are encouraging, showing high accuracy in grouping tweets based on their contents. We have also performed a comparative analysis of the Cosine similarity and Euclidean distance based similarity graph generation, and it is found that the Cosine similarity yields better results than the Euclidian distance measure.

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“…Techniques and methods help in the information retrieval process, use pattern matching and keyword combinations (WONG, 2012), but it is not yet developed enough to provide the existing concepts about the relationships between data (ABULAISH; ANWAR, 2012). This way, the available textual data is associated with the access challenge due to its unstructured nature.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Extraction From Scientific Articles: A Proposal for Initial Research Methodology on Text Mining

Ancioto¹,

Dantas²

2022

JER

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The need to extract textual information encourages the development of automated tools for document reading, pattern recognition and knowledge extraction. The large amount of scientific content available on the internet makes it difficult to search for the most relevant and up-to-date information. In this work, a research methodology is performed to support the process of information retrieval in a scientific environment with the initial theme of Text Mining. Steps were defined for searching, visualizing and filtering data, through computational tools, with the objective of finding relevant scientific material with assertiveness. The method resulted in a list of 49 published articles with an average impact factor of 2.193, material that was compiled and used for initial mapping of the topic. It is concluded that the initial research methodology proposal provides results with a reduced time, with relevance to the material and that the use of general-purpose computational tools, even with a lot of manual intervention, helps to define the initial state of a scientific research. Text mining can make the extraction of knowledge from scientific research already produced more productive, promoting the identification of the state of the art and research gaps.

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Deep Text Mining for Automatic Keyphrase Extraction from Text Documents

Cited by 9 publications

References 10 publications

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning

Content-Based Tweets Clustering and Analysis

Knowledge Extraction From Scientific Articles: A Proposal for Initial Research Methodology on Text Mining

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