Keyphrase Extraction Algorithm Research Articles

Comparative Analysis on Automatic Keyphrase Extraction (AKPE) Techniques

Abstract— Extracting Keyphrase from the large corpus manually is a tedious task. To overcome this challenging task Automatic Keyphrase Extraction (AKPE) techniques have been used for Keyphrase extraction. Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrase constitutes a succinct conceptual summary of a document, which is very useful in digital information management systems for semantic indexing, faceted search, document clustering, and classification. This paper contains a brief description of automatic Keyphrase extraction techniques which consist of various algorithms and also a comparative study on automotive Keyphrase extraction techniques such as KEA, TF-IFD, RAKE, TextRank. Keywords— Keyphrase Extraction, Techniques, Comparison, Keyphrase Extraction Algorithm, Term Frequency- Inverse Frequency Document, Rapid Automatic Keyphrase Extraction, TextRank.

A method of identifying domain-specific academic user information needs based on academic Q&A communities

PurposeTo address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.Design/methodology/approachThis study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.FindingsExperimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.Originality/valueThis study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

BibRank: Automatic Keyphrase Extraction Platform Using Metadata

Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplification. This paper introduces a platform that integrates keyphrase datasets and facilitates the evaluation of keyphrase extraction algorithms. The platform includes BibRank, an automatic keyphrase extraction algorithm that leverages a rich dataset obtained by parsing bibliographic data in BibTeX format. BibRank combines innovative weighting techniques with positional, statistical, and word co-occurrence information to extract keyphrases from documents. The platform proves valuable for researchers and developers seeking to enhance their keyphrase extraction algorithms and advance the field of natural language processing.

A Hybrid Semantic Networks Construction Framework for Engineering Design

Abstract This paper proposes a novel framework for building semantic networks from a seed design statement using Recursive Object Modeling (ROM), Word2Vec language modeling, and vector semantic-based method. Semantic Scholar API was used to retrieve abstracts of scientific papers to build ROM-based Semantic Networks to address the design problem implied in the seed design statement, following Environment Analysis from Environment-Based Design (EBD) methodology. The proposed framework was applied to construct the semantic network for a project to design aircraft braking systems, which demonstrates the framework's efficiency. The presented research makes two major contributions: a ROM-based phrase extractor and a domain-specific language model, which is trained on the automatically collected literature abstracts. Using a manually created and assessed truth set containing 100 pairs of abstract-key phrases, the phrase extractor was evaluated by benchmarking it with two existing off-the-shelf key phrase extraction algorithms: TextRank and Rake. The ROM-based phrase extractor extracted most key phrases from target domains and showed higher precision, recall, and F-1 scores than other methods. Meanwhile, the trained project-specific language model was evaluated using the NASA thesaurus. We randomly sampled 457 pairs of connected domain-specific terms related to aircraft braking and landing knowledge. Our Skip-gram model was compared with Google's pre-trained word2vec model and a baseline word2vec model. The results demonstrated that our language model could detect the most pairs of concepts from the NASA thesaurus. The generated semantic network can be applied to design information retrieval, computer-aided design idea generation, cross-domain communication support system, and designer training tool.

Otomind: A Text Mining Approach to Automatic Concept Mapping

Despite the linear use of PowerPoint presentation slides in classroom and lecture halls, the brain works primarily with key concepts that it links and integrates. This is a big contrast from the typical student behaviour of reading or memorizing the slides.This paper presents a new system that automatically generate a mind map based on a set of keywords and keyphrases extracted from PowerPoint presentation slides. The system takes a text mining approach and relies on a keyphrase extraction algorithm to extract the keywords and keyphrases. Survey results reflect a rising need of such application as part of educational technologies and the potential benefits are observed and discussed.

Using Machine Learning to Improve Lead Times in the Identification of Emerging Customer Needs

In recent years, computational approaches for automatically extracting the voice of the customer from user generated content have been proposed. These studies have tackled the task of obtaining current customer needs, however, there is a lack of methods that predict future needs (i.e. needs that may become popular in the marketplace). Therefore, this study presents a multi-document keyphrase extraction algorithm which predicts future customer needs from users’ social media posts on Reddit. Key to our approach is a novel document filtering method (discovering potentially relevant social media content) and a keyphrase ranking method, which promotes terms with rising frequency likely to be future product needs. In order to evaluate the approach, a case study of “toothpaste” needs is reviewed and a novel evaluation approach using ground truth automatically extracted from a collection of future specifications of new-to-market products is proposed. In our evaluation, we show that the approach is significantly better than simple baselines at identifying customer needs on social media before they trend in the marketplace. We also show that our approach can capture important customer needs identified by a large multinational company with lead times of up to 25 months ahead of them trending in the marketplace. The findings of this research could provide many benefits to businesses such as gaining early access into markets ahead of their competitors and giving early notice to manufacturers/engineers/developers before a need for a product is in demand.

Keyphrase Extraction by Improving TextRank with an Integration of Word Embedding and Syntactic Information

Background: As a known key phrase extraction algorithm, TextRank is an analogue of PageRank algorithm, which relied heavily on the statistics of term frequency in the manner of co-occurrence analysis. Objective: The frequency-based characteristic made it a neck-bottle for performance enhancement, and various improved TextRank algorithms were proposed in the recent years. Most of improvements incorporated semantic information into key phrase extraction algorithm and achieved improvement. Method: In this research, taking both syntactic and semantic information into consideration, we integrated syntactic tree algorithm and word embedding and put forward an algorithm of Word Embedding and Syntactic Information Algorithm (WESIA), which improved the accuracy of the TextRank algorithm. Results: By applying our method on a self-made test set and a public test set, the result implied that the proposed unsupervised key phrase extraction algorithm outperformed the other algorithms to some extent.

LVTIA: A new method for keyphrase extraction from scientific video lectures

Due to the growth of technology, the expansion of communication infrastructure and crises of COVID-19 pandemic, e-learning and virtual education is expanding. One of the best ways to access and organize these information is indexing using automatic intelligent methods. Indexing requires assigning keywords or keyphrases to each video, to represent its content. The main focus of this research is to propose an approach by which appropriate keyphrases are assigned to scientific video lectures. For this purpose, a new algorithm called LVTIA, Lecture Video Text mining-base Indexing Algorithm, is proposed in which the textual content of video frames along with the text extracted from audio signal are merged together, and a new keyphrase extraction method is proposed. The proposed method considers new local and global features for each candidate phrases, along with a new feature reflecting the occurrence of each phrase in the audio signals or video frames. The method is implemented using five distinct data sets in English and Persian. The results are evaluated based on precision, recall, F1-measure and MAP@K metrics and compared with some of the well-known keyphrase extraction algorithms. Based on the results, the best MAP@K for English videos is related to LVTIA algorithm with the values of, 0.7912, 0.8069, 0.8069 for k=5,10,15, respectively. In addition, LVTIA is able to provide best MAP@K for Persian videos which are 0.6367, 0.6866, 0.6874 for k=5,10,15, respectively. According to Friedman nonparametric statistical test, the performance of different algorithms in precision, recall, F1-measure metrics, are statistically different from LVTIA as well.

A comparative study of keyword extraction algorithms for English texts

Abstract This study mainly analyzed the keyword extraction of English text. First, two commonly used algorithms, the term frequency–inverse document frequency (TF–IDF) algorithm and the keyphrase extraction algorithm (KEA), were introduced. Then, an improved TF–IDF algorithm was designed, which improved the calculation of word frequency, and it was combined with the position weight to improve the performance of keyword extraction. Finally, 100 English literature was selected from the British Academic Written English Corpus for the analysis experiment. The results showed that the improved TF–IDF algorithm had the shortest running time and took only 4.93 s in processing 100 texts; the precision of the algorithms decreased with the increase of the number of extracted keywords. The comparison between the two algorithms demonstrated that the improved TF–IDF algorithm had the best performance, with a precision rate of 71.2%, a recall rate of 52.98%, and an F 1 score of 60.75%, when five keywords were extracted from each article. The experimental results show that the improved TF–IDF algorithm is effective in extracting English text keywords, which can be further promoted and applied in practice.

ISKE: An unsupervised automatic keyphrase extraction approach using the iterated sentences based on graph method

The automatic extraction of key information is necessary for knowledge discovery in this era of rapid knowledge growth. The extraction of key information can also help researchers quickly obtain the information they want instead of reading through all potential documents. Recently, researchers have refocused their attention from words to sentences because utilizing sentences outperforms with respect to illustrating semantics and reduces the calculation complexity. We present a novel and lightweight automatic keyphrase extraction algorithm that does not depend on any external resources, including an external dictionary or corpus. Unlike traditional graph-based algorithms that iterate words to generate keyphrase lists, our proposal uses iterated sentences to rank words and generate keyphrase lists for the semantic information of sentences that are more complete than the word. We initialize the values of words with weighted information and generate a sentence score using these values. Then, we integrate sentences to update their values; hence, the values of the words are updated with the sentence information. We iterate this process until the values of the sentences and words converge. The proposed method is based on a measurement of the relations between sentences and an evaluation of the flow of these relations in an easily understood manner. These relationships are based on the hypothesis that the causality between adjacent sentences is semantically stronger than the causality between words. We not only increase the extraction accuracy, but also reduce the number of iterations of the algorithm. We compare our proposed method with five strong, popular baseline algorithms on four datasets. The results show that our proposed method performs better than the other algorithms on three evaluation metrics.

TripleRank: An unsupervised keyphrase extraction algorithm

Automatic keyphrase extraction algorithms aim to identify words and phrases that contain the core information in documents. As online scholarly resources have become widespread in recent years, better keyphrase extraction techniques are required to improve search efficiency. We present two features, keyphrase semantic diversity and keyphrase coverage, to overcome limitations of existing methods for unsupervised keyphrase extraction. Keyphrase semantic diversity is the degree of semantic variety in the extraction result, which is introduced to avoid extracting synonym phrases that contain the same high-score candidate. Keyphrase coverage refers to candidates’ representativeness of other words in documents. We propose an unsupervised keyphrase extraction method called TripleRank, which evaluates three features: word position (a sensitive feature for academic documents) and two innovative features mentioned above. The architecture of TripleRank includes three sub-models that score the three features and a summing model. Though involving multiple models, there is no typical iteration process in TripleRank; hence, the computational cost is relatively low. TripleRank has led the experiment results on four academic datasets compared to four state-of-the-art baseline models, which confirmed the influence of keyphrase semantic diversity and keyphrase coverage and proved the efficiency of our method.

Document Specific Supervised Keyphrase Extraction With Strong Semantic Relations

Keyphrase extraction is the task of automatically extracting descriptive phrases or concepts that represent the main topics in a document. Finding good keyphrases in a document can quickly summarize knowledge for information retrieval and decision making. Existing keyphrase extraction methods cannot be customized to each specific document, and cannot capture flexible semantic relations. In this paper, a keyphrase extraction algorithm using maximum sequential pattern mining with one-off and general gaps condition, called Ke-MSMING, is presented. Ke_MSMING first searches all keyphrase candidates from a document using sequential patterns mining and the topic model, and then adopts supervised machine learning to classify each keyphrase candidate as a keyphrase or not. Finally, Ke_MSMING selects top-N keyphrases as the final keyphrases. Ke_MSMING not only uses baseline features and pattern features but also uses centrality features obtained from the cooccurrence semantic network, and the cooccurrence networks can yield powerful semantic relations for keyphrase extraction. Experimental results on two datasets demonstrate that Ke_MSMING has better performance than other state-of-the-art keyphrase extraction approaches.

Charismatic Document Clustering Through Novel K-Means Non-negative Matrix Factorization (KNMF) Algorithm Using Key Phrase Extraction

The tedious challenging of Big Data is to store and retrieve of required data from the search engines. Problem Defined There is an obligation for the quick and efficient retrieval of useful information for the many organizations. The elementary idea is to arrange these computing files of organization into individual folders in an hierarchical order of folders. Manually, to order these files into folders, there is an ardent need to know about the file contents and name of the files to give impression of files, so that it provides an alignment of certain set of files as a bunch. Problem Statement Manual grouping of files has its own complications, for example when these files are in numerous amounts and also their contents cannot be illustrious by their labels. Therefore, it’s an intense requirement for Document clustering with data processing machines for enthusiastic results. Existing System A couple of analyzers are impending with dynamic algorithms and comprehensive analogy of extant algorithms, but, yet, these have been restricted to organizations and colleges. After recent updated rules of NMF their raised a self interest in document clustering. These rules gave trust in its performances with better results when compared to Latent Semantic Indexing with Singular Value Decomposition. Proposed System A new working miniature called Novel K-means Non-Negative Matrix Factorization (KNMF) is implemented using renovated guidelines of NMF which has been diagnosed for clustering documents consequently. A new data set called Newsgroup20 is considered for the exploratory purpose. Removal of common clutter/stop words using keywords from Key Phrase Extraction Algorithm and a new proposed Iterated Lovin stemming will be utilized in preprocessing step inassisting to KNMF. Compared to the Porter stemmer and Lovins stemmer algorithms, Iterative Lovins algorithm is providing 5% more reduction. 60% of the document terms are been minimized to root as remaining terms are already root words. Eventually, an appeal to these processes named “Progressive Text mining radical” is developed inlateral exertion of K-Means algorithm from the defined Apache Mahout Project which is used to analyze the performance of the MapReduce framework in Hadoop.

Автоматическое извлечение ключевых слов и словосочетаний из русскоязычных текстов с помощью алгоритма KEA

В докладе представлены результаты работы по модификации алгоритма KEA ( Keyphrase Extraction Algorithm ), используемого для извлечения ключевых слов и словосочетаний. KEA широко известен своей эффективностью для извлечения ключевых слов и словосочетаний из англоязычных текстов. В статье представлены результаты применения данного алгоритма к текстам на русском языке. Для определения качества работы алгоритма с русскоязычными текстами были проведены эксперименты на материале представительных корпусов.

Performance Analysis of Graph based Keyphrase Extraction metrics for uncertain User-generated data

The keyphrase extraction, text summarization and word collocation algorithms have been different forms of summarizing the textual information. However, the unstructured user-generated data over social media is short-text and ill-formed. The use of existing traditional keyphrase extraction algorithm for unstructured data may give indifferent results. This problem has received minimal attention from academic researchers and practitioners. Thus in this article, the experimental evaluation of existing keyphrase extraction techniques have been performed. The performance evaluation have been done using ROUGE-1, ROUGE-2 and ROUGE-L score. The evaluation measures gives useful insights about keyphrase extraction techniques for social media data. The application of keyphrase extraction include topic detection, identifying events, trend detection and microblogs summarization.

Key Concept Identification: A Sentence Parse Tree-Based Technique for Candidate Feature Extraction From Unstructured Texts

The effectiveness of automatic key concept or keyphrase identification from unstructured text documents mainly depends on a comprehensive and meaningful list of candidate features extracted from the documents. However, the conventional techniques for candidate feature extraction limit the performance of keyphrase identification algorithms and need improvement. The objective of this paper is to propose a novel parse tree-based approach for candidate feature extraction to overcome the shortcomings of the existing techniques. Our proposed technique is based on generating a parse tree for each sentence in the input text. Sentence parse trees are then cut into sub-trees to extract branches for candidate phrases (i.e., noun, verb, and so on). The sub-trees are combined using parts-of-speech tagging to generate the flat list of candidate phrases. Finally, filtering is performed using heuristic rules and redundant phrases are eliminated to generate final list of candidate features. Experimental analysis is conducted for validation of the proposed scheme using three manually annotated and publicly available data sets from different domains, i.e., Inspec, 500N-KPCrowed, and SemEval-2010. The proposed technique is fine-tuned to determine the optimal value for the parameter context window size and then it is compared with the existing conventional n-gram and noun-phrase-based techniques. The results show that the proposed technique outperforms the existing approaches and significant improvements of 13.51% and 30.67%, 12.86% and 5.48%, and 13.16% and 31.46% are achieved, in terms of precision , recall , and F-measure when compared with noun-phrase-based scheme and n-gram-based scheme, respectively. These results give us confidence to further validate the proposed technique by developing a keyphrase extraction algorithm in the future.

News Headline Building using Hybrid Headline Generation Technique for Quick Gist

A considerable amount of time is required to interpret whole news article to get the gist of it. Therefore, in order to reduce the reading and interpretation time, headlines are necessary. The available techniques for news headline construction mainly includes extractive and abstractive headline generation techniques. In this paper, context based news headline is formed from long news article by using techniques of core Natural Language Processing (NLP) and key terms of news article. Key terms are retrieved from lengthy news article by using various approaches of keyword extraction. The keyphrases are picked out using Keyphrase Extraction Algorithm (KEA) which helps to construct headline syntax along with NLP's parsing technique. Sentence compression algorithm helps to generate compressed sentences from generated parse tree of leading sentences. Headline helps user for reducing cognitive burden of reader by reflecting important contents of news. The objective is to frame headline using key terms for reducing reading time and efforts of reader.

Multimodal Retrieval using Mutual Information based Textual Query Reformulation

Multimodal Retrieval is a well-established approach for image retrieval. Usually, images are accompanied by text caption along with associated documents describing the image. Textual query expansion as a form of enhancing image retrieval is a relatively less explored area. In this paper, we first study the effect of expanding textual query on both image and its associated text retrieval. Our study reveals that judicious expansion of textual query through keyphrase extraction can lead to better results, either in terms of text-retrieval or both image and text-retrieval. To establish this, we use two well-known keyphrase extraction techniques based on tf-idf and KEA. While query expansion results in increased retrieval efficiency, it is imperative that the expansion be semantically justified. So, we propose a graph-based keyphrase extraction model that captures the relatedness between words in terms of both mutual information and relevance feedback. Most of the existing works have stressed on bridging the semantic gap by using textual and visual features, either in combination or individually. The way these text and image features are combined determines the efficacy of any retrieval. For this purpose, we adopt Fisher-LDA to adjudge the appropriate weights for each modality. This provides us with an intelligent decision-making process favoring the feature set to be infused into the final query. Our proposed algorithm is shown to supersede the previously mentioned keyphrase extraction algorithms for query expansion significantly. A rigorous set of experiments performed on ImageCLEF-2011 Wikipedia Retrieval task dataset validates our claim that capturing the semantic relation between words through Mutual Information followed by expansion of a textual query using relevance feedback can simultaneously enhance both text and image retrieval.

THE MECHANICS OF THE PRESENTATION MINING FRAMEWORK

This paper presents the mechanics of a presentation mining system that mines keywords and key phrases from a collection of PowerPoint slides and generates a mind map using the extracted words and phrases. The core of presentation mining lies in two stages; ranking the potential phrases and extracting the keywords and key phrases. The keywords and key phrases form a mind map, which is then evaluated against a domain ontology. The results of recall and precision are also compared between the existing key phrase extraction system called the KP-Miner and the proposed presentation mining system. The key phrase extraction algorithm by the proposed presentation mining system achieved higher recall and precision than KP-Miner, hence producing a more accurate visualization of the PowerPoint slides in the form of mind map.

Consensus-based Approach for Keyword Extraction from Urban Events Collections

Automatic keyword extraction (AKE) from textual sources took a valuable step towards harnessing the problem of efficient scanning of large document collections. Particularly in the context of urban mobility, where the most relevant events in the city are advertised on-line, it becomes difficult to know exactly what is happening in a place.In this paper we tackle this problem by extracting a set of keywords from different kinds of textual sources, focusing on the urban events context. We propose an ensemble of automatic keyword extraction systems KEA (Key-phrase Extraction Algorithm) and KUSCO (Knowledge Unsupervised Search for instantiating Concepts on lightweight Ontologies) and Conditional Random Fields (CRF).Unlike KEA and KUSCO which are well-known tools for automatic keyword extraction, CRF needs further pre-processing. Therefore, a tool for handling AKE from the documents using CRF is developed. The architecture for the AKE ensemble system is designed and efficient integration of component applications is presented in which a consensus between such classifiers is achieved. Finally, we empirically show that our AKE ensemble system significantly succeeds on baseline sources and urban events collections.