Web Document Clustering by Using Automatic Keyphrase Extraction

Han, Juhyun; Kim, Tae‐Hwan; Choi, Joongmin

doi:10.1109/wi-iatw.2007.46

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…Unlike KEA, KeyRank algorithm performs outclass and provides good results ( Wang, Sheng & Hu, 2017 ). The key phrase extraction algorithm also effectively identifies the number of clusters in a massive number of documents ( Han, Kim & Choi, 2007 ). To mine top-quality keyphrases from the text, Wang, Mu & Fang (2008) presented a technique.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Automatic computer science domain multiple-choice questions generation based on informative sentences

Maheen

Asif

Ahmad

et al. 2022

PeerJ Computer Science

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Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generation, respectively. The overall MCQ generation achieved 80% accuracy through this system by the experts. Finally, a desktop app was developed that takes the contents in textual form as input, processes it at the backend, and visualizes the generated MCQs on the interface. The presented solution may help teachers, students, and other stakeholders with automatic MCQ generation.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Automatic computer science domain multiple-choice questions generation based on informative sentences

Maheen

Asif

Ahmad

et al. 2022

PeerJ Computer Science

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“…They refer to this factor as the UA. They also used an aging theory developed by Chen et al [4] to create, grow, and destroy a topic. The life cycles of the topics are tracked by using an energy function.…”

Section: Learning To Rankmentioning

confidence: 99%

NewSociRank: Recognizing and Ranking Frequent News Topics Using Social Media Factors

Harshitha¹,

Rafi²

2018

IJTSRD

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Mass media sources such as news media used to inform us of daily events before. Now a day, unlike news media, social media services like Twitter provide a huge amount of user-generated data, which contain informative news-related content. For these resources to be useful, we need to find a way to filter the noise and capture only the content based on its similarity to the news media. However, even after noise is removed, information overload may still exist in the remaining data-hence, it is convenient to prioritize it for consumption. To achieve prioritization, the information must be ranked in order of estimated importance considering three factors. First, the media focus(MF) of a topic, the temporal prevalence of a particular topic in the news media. Second, user attention (UA), the temporal prevalence of the topic in social media. Last, the interaction between the social media users who mention this topic indicates the strength of the community discussing it, and can be regarded as the user interaction (UI) toward the topic. We propose an unsupervised framework-NewSociRank-which recognizes the news topics prevalent(common) in both social media and the news media, and then ranks them by relevance(popularity) using their degrees of MF, UA, and UI.

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“…Document keyphrases are used successfully in information retrieval (IR) and natural language processing (NLP) tasks such as document indexing,6 clustering,7,8 classification,9 and summarization 10–12. Furthermore, keyphrases are well exploited for other tasks such as thesaurus creation,13,14 subject metadata enrichment,15 query expansion 16,17.…”

Section: Background and Related Workmentioning

confidence: 99%

Automatic keyphrase extraction and ontology mining for content-based tag recommendation

Pudota

Dattolo

Baruzzo

et al. 2010

Int. J. Intell. Syst.

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Collaborative tagging represents for the Web a potential way for organizing and sharing information and for heightening the capabilities of existing search engines. However, because of the lack of automatic methodologies for generating the tags and supporting the tagging activity, many resources on the Web are deficient in tag information, and recommending opportune tags is both a current open issue and an exciting challenge. This paper approaches the problem by applying a combined set of techniques and tools (that uses tags, domain ontologies, keyphrase extraction methods) thereby generating tags automatically. The proposed approach is implemented in the PIRATES (Personalized Intelligent tag Recommender and Annotator TEStbed) framework, a prototype system for personalized content retrieval, annotation, and classification. A case study application is developed using a domain ontology for software engineering

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Web Document Clustering by Using Automatic Keyphrase Extraction

Cited by 12 publications

References 4 publications

Automatic computer science domain multiple-choice questions generation based on informative sentences

Automatic computer science domain multiple-choice questions generation based on informative sentences

NewSociRank: Recognizing and Ranking Frequent News Topics Using Social Media Factors

Automatic keyphrase extraction and ontology mining for content-based tag recommendation

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