FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data

Abstract

Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n-grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.

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

“…A phrase contains more information than a word, so our system requires a phrase mining technique to extract quality phrases from the underlying domain. Similar works have been done previously using N-gram ( Tahir et al, 2021 ) and topical phrase mining techniques. In detail, the N-gram technique facilitates identification and extracts frequent N-grams from the given text ( Tahir et al, 2021 ).…”

“…Similar works have been done previously using N-gram ( Tahir et al, 2021 ) and topical phrase mining techniques. In detail, the N-gram technique facilitates identification and extracts frequent N-grams from the given text ( Tahir et al, 2021 ). Similarly, topical phrase mining is a helpful technique for phrase mining, topic identification, social event discovery, etc .…”

“…This helps to get the sentences closest to the centroid for creating a summary. The sentences are then scored on several features like quality phrases ( Liu, Shang & Han, 2017 ; Tahir et al, 2021 ), TF-IDF ( Wu et al, 2008 ), number of nouns and verbs, number of stop words, and Jaccard similarity of chapter title with candidate stems. Since readily available systems for generating MCQs lack informative sentences, the quality of generated distractors is low.…”

A study on the classification of stylistic and formal features in English based on corpus data testing

“…Duan & Huang (2016) selected data sets describing plant species samples with more than 70,000 words as language materials and applied the N-gram algorithm to explore the automatic recognition of new words in professional fields. Based on Japanese word rules and onomatopoeia patterns, Tahir et al (2021) proposed to add new nodes to the case framework of sentences to find the optimal path and identify the unknown neologisms in Japanese. The method has high accuracy for specific fields but shows poor portability, and the rule-building process requires a lot of human and material resources.…”

Impact analysis of keyword extraction using contextual word embedding

“…A keyword or key phrase is a word that precisely and accurately describes the content of a document, in whole or in part ( Tahir et al, 2021 ). A keyword is a unigram, while a key phrase is an N-gram, i.e., more than one word.…”