Automatic Keyphrase Extraction from Medical Documents

Sarkar, Kamal

doi:10.1007/978-3-642-11164-8_44

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…Keyphrase extraction is also useful for generating questions from documents. (Subramanian et al 2017) used a twostage framework to generate questions from documents using extracted keyphrases. Furthermore, keyphrase extraction was applied to different kinds of text: news articles (Marujo et al ), scientific articles (Nguyen and Kan 2007), medical documents (Sarkar 2009), or online policies (Audich, Dara, and Nonnecke 2016).…”

Section: Related Workmentioning

confidence: 99%

Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

Banjade

Oli

Tamang³

et al. 2021

FLAIRS

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We present a novel approach to intro-to-programming domain model discovery from textbooks using an over-generation and ranking strategy. We first extract candidate key phrases from each chapter in a Computer Science textbook focusing on intro-to-programming and then rank those concepts according to a number of metrics such as the standard tf-idf weight used in information retrieval and metrics produced by other text ranking algorithms. Specifically, we conduct our work in the context of developing an intelligent tutoring system for source code comprehension for which a specification of the key programming concepts is needed - the system monitors students' performance on those concepts and scaffolds their learning process until they show mastery of the concepts. Our experiments with programming concept instruction from Java textbooks indicate that the statistical methods such as KP Miner method are quite competitive compared to other more sophisticated methods. Automated discovery of domain models will lead to more scalable Intelligent Tutoring Systems (ITSs) across topics and domains, which is a major challenge that needs to be addressed if ITSs are to be widely used by millions of learners across many domains.

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Section: Related Workmentioning

confidence: 99%

Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

Banjade

Oli

Tamang³

et al. 2021

FLAIRS

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“…In [26], Zhang et al performed this work for multi-word extraction using augmented mutual information. Sarkar [21] has proposed a supervised machine learning method based on KEA to extract keyphrases from biomedical documents. He used the GENIA tagger to parse text documents for candidate phrase extraction.…”

Section: State-of-the-art In Keyphrase Extraction From Text Documentsmentioning

confidence: 99%

Deep Text Mining for Automatic Keyphrase Extraction from Text Documents

Abulaish¹,

Jahiruddin²,

Dey³

2011

Journal of Intelligent Systems

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Due to existence of a huge amount of textual data either on the World Wide Web or in textual databases like PubMed, the development of novel automatic keyphrase extraction methods has emerged as one of the key research problems in recent past. Consequently, a number of machine learning techniques, mostly supervised, have been proposed to extract keyphrases from text documents. But, one of the main bottlenecks that hinders the success of such systems is the requirement of annotated corpora for training purpose. In this paper, we propose the design of a deep text mining system to identify keyphrases in text documents that are either unstructured or semi-structured in nature. The novelty of our system lies in its applicability on a single document, instead of demanding a collection of annotated texts for training, to identify keyphrases embedded within it. The proposed system applies parsing techniques to identify candidate phrases. After mapping the original set of candidate phrases into a low-dimensional space using Singular Value Decomposition (SVD), the Markov Clustering (MCL) technique is applied to cluster related sentences together. Finally, considering each cluster as a document, Latent Dirichlet Allocation (LDA) is applied to identify feasible keyphrases that are presented to users in non-increasing order of their relevance score values. The efficacy of the proposed system is established through experimentation on datasets from two different domains. On comparative evaluation, we found that the proposed system outperforms KEA and KEA++ that apply the supervised machine learning approach for automatic keyphrase extraction from text documents.

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“…They argue that combining several base classifiers yields better results. Sarkar [28] used naïve Bayes classifier to extract keyphrases from medical documents; so his work is domain-specific and utilizes a glossary database.…”

Section: Keyphrase Extraction For Non-arabic Textsmentioning

confidence: 99%

Automatic keyphrase extraction for Arabic news documents based on KEA system

Duwairi

Hedaya

2016

IFS

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A keyphrase is a sequence of words that play an important role in the identification of the topics that are embedded in a given document. Keyphrase extraction is a process which extracts such phrases. This has many important applications such as document indexing, document retrieval, search engines, and document summarization. This paper presents a framework for extracting keyphrases from Arabic news documents which is based on the KEA system. It relies on supervised learning, Naïve Bayes in particular, to extract keyphrases. Two probabilities are computed: the probability of being a keyphrase and the probability of not being a keyphrase. The final set of keyphrases is chosen from the set of phrases that have high probabilities of being keyphrases. The novel contributions of the current work are that it provides insights on keyphrase extraction for news documents written in Arabic. It also presents an annotated dataset that was used in the experimentation. Finally, it uses Naïve Bayes as a medium for extracting keyphrases.

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Automatic Keyphrase Extraction from Medical Documents

Cited by 8 publications

References 6 publications

Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

Domain Model Discovery from Textbooks for Computer Programming Intelligent Tutors

Deep Text Mining for Automatic Keyphrase Extraction from Text Documents

Automatic keyphrase extraction for Arabic news documents based on KEA system

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