Text classification is a domain with high dimensional feature space.Extracting the keywords as the features can be extremely useful in text classification.An empirical analysis of five statistical keyword extraction methods.A comprehensive analysis of classifier and keyword extraction ensembles.For ACM collection, a classification accuracy of 93.80% with Bagging ensemble of Random Forest. Automatic keyword extraction is an important research direction in text mining, natural language processing and information retrieval. Keyword extraction enables us to represent text documents in a condensed way. The compact representation of documents can be helpful in several applications, such as automatic indexing, automatic summarization, automatic classification, clustering and filtering. For instance, text classification is a domain with high dimensional feature space challenge. Hence, extracting the most important/relevant words about the content of the document and using these keywords as the features can be extremely useful. In this regard, this study examines the predictive performance of five statistical keyword extraction methods (most frequent measure based keyword extraction, term frequency-inverse sentence frequency based keyword extraction, co-occurrence statistical information based keyword extraction, eccentricity-based keyword extraction and TextRank algorithm) on classification algorithms and ensemble methods for scientific text document classification (categorization). In the study, a comprehensive study of comparing base learning algorithms (Naive Bayes, support vector machines, logistic regression and Random Forest) with five widely utilized ensemble methods (AdaBoost, Bagging, Dagging, Random Subspace and Majority Voting) is conducted. To the best of our knowledge, this is the first empirical analysis, which evaluates the effectiveness of statistical keyword extraction methods in conjunction with ensemble learning algorithms. The classification schemes are compared in terms of classification accuracy, F-measure and area under curve values. To validate the empirical analysis, two-way ANOVA test is employed. The experimental analysis indicates that Bagging ensemble of Random Forest with the most-frequent based keyword extraction method yields promising results for text classification. For ACM document collection, the highest average predictive performance (93.80%) is obtained with the utilization of the most frequent based keyword extraction method with Bagging ensemble of Random Forest algorithm. In general, Bagging and Random Subspace ensembles of Random Forest yield promising results. The empirical analysis indicates that the utilization of keyword-based representation of text documents in conjunction with ensemble learning can enhance the predictive performance and scalability of text classification schemes, which is of practical importance in the application fields of text classification.

Ensemble of keyword extraction methods and classifiers in text classification

Millennium Institute for Foundational Research on Data (IMFD)
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT), CONICYT FONDECYT: 1181896

Information extraction meets the semantic web: a survey

The second edition of the International Encyclopedia of Information and Library Science (IEILS) by Feather and Sturges has been substantially revised and updated to include rapid changes in technology since the first edition was published in 1997. However, the two editions follow the same basic format. The main body of the work is an alphabetical arrangement of subjects or terms found in the fields of information and library science. The entries vary in length from brief definitions of a few sentences to long, signed articles that are followed by numerous references and recommendations for further reading.

International Encyclopedia of Information and Library Science

The goal of text generation is to make machines express in human language. It is one of the most important yet challenging tasks in natural language processing (NLP). Since 2014, various neural encoder-decoder models pioneered by Seq2Seq have been proposed to achieve the goal by learning to map input text to output text. However, the input text alone often provides limited knowledge to generate the desired output, so the performance of text generation is still far from satisfaction in many real-world scenarios. To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models. This research direction is known as knowledge-enhanced text generation. In this survey, we present a comprehensive review of the research on knowledge enhanced text generation over the past five years. The main content includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data. This survey can have broad audiences, researchers and practitioners, in academia and industry.

A Survey of Knowledge-Enhanced Text Generation.

Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called KeyPhrase Extraction (KePhEx) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI, is derived which denotes a given node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores.

/pdf/teket-a-tree-based-unsupervised-keyphrase-extraction-9k7swg90fa.pdf

TeKET : a Tree-Based Unsupervised Keyphrase Extraction Technique

In this paper we present a survey of various techniques available in text mining for keyword and keyphrase extraction.

/pdf/keyword-and-keyphrase-extraction-techniques-a-literature-5i7hbc18g4.pdf

Keyword and Keyphrase Extraction Techniques: A Literature Review

Most of the research in the field of information retrieval (IR) has focused on the English language, but recently there has been a considerable amount of work and effort to develop IR systems for languages other than English. Research and experimentation in the field of IR in the Hindi language are relatively new and limited compared to the research that has been done in English, which has been dominant in the field of IR for a long while. A fundamental tool in IR is the employment of stop word lists. Stop words have no retrieval value in IR. Till now, many stop word lists have been developed for English, European and Chinese languages. However, there is no standard stop word list which has been constructed for Hindi language. In this paper an approach to construct a generic stop word list for Hindi language have been presented. Our list contains more than 800 stop words.

http://accentsjournals.org/PaperDirectory/Journal/IJACR/2018/1/3.pdf

Construction of a generic stopwords list for Hindi language without corpus statistics

In this paper we propose an unsupervised, domain independent as well as corpus independent approach for automatic keyword extraction. In second part of the paper we have suggested an extension of the approach to extract keyphrases from the document. The approach is general and can be applied to any language. However, we have tested the approach on Hindi language. Our approach combines the information contained in frequency and spatial distribution of a word in order to extract keywords from a document. Our work is especially significant in the light that it has been implemented and tested on Hindi which is a resource poor and underrepresented language.

Keyword and keyphrase extraction from single Hindi document using statistical approach

Keywords of a document give us an idea about its important points without going through the whole text. In this paper, we propose an unsupervised, domain-independent, and corpus-independent approach for automatic keyword extraction. The approach is general and can be applied to any language. However, we have tested the approach on Hindi language. Our approach combines the information contained in frequency and spatial distribution of a word in order to extract keywords from a document. Our work is specially significant in the light that it has been implemented and tested on Hindi which is a resource poor and underrepresented language.

Keyword Extraction from Hindi Documents Using Statistical Approach

extraction is an important task in text mining. In this paper a novel, unsupervised, domain independent and language independent approach for automatic keyword extraction from single documents have been proposed. We have used the word intermediate distance vector and its mean value to extract keywords. We have compared our approach with results from the standard deviation of intermediate distances approach as standard and found that there is heavy overlapping between the results of both approaches with the advantage that our approach is faster, especially in case of long documents as it removes the need to compute the standard deviation of word intermediate distance vector. Two famous works viz. "Origin of Species" and "A Brief History of Time" to demonstrate the experimental results have been used. Experiments show that the proposed approach works almost as better as the standard deviation approach and the percentage overlap between top 30 extracted keywords is more than 50%.

https://accentsjournals.org/PaperDirectory/Journal/IJACR/2016/7/3.pdf

Sifatullah Siddiqi

Papers

Keyword and Keyphrase Extraction Techniques: A Literature Review

Construction of a generic stopwords list for Hindi language without corpus statistics

Keyword and keyphrase extraction from single Hindi document using statistical approach

Keyword Extraction from Hindi Documents Using Statistical Approach

Keyword extraction from single documents using mean word intermediate distance