Comparison of Vector Space Representations of Documents for the Task of Information Retrieval of Massive Open Online Courses

Klenin, Julius; Botov, Dmitry; Dmitrin, Yuri

doi:10.1007/978-3-319-71746-3_14

Cited by 5 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although feature selection techniques reduce the dimensionality of textual data up to great extent, however, traditional machine learning based text document classification methodologies still face the sparsity problem in bag of words based feature representation techniques [27], [28]. Bag of words based feature representation techniques consider unigrams, n-grams or specific patterns as features [27], [28]. These algorithms do not capture the complete contextual information of data and also face the problem of data sparsity [27], [28].…”

Section: Introductionmentioning

confidence: 99%

“…Bag of words based feature representation techniques consider unigrams, n-grams or specific patterns as features [27], [28]. These algorithms do not capture the complete contextual information of data and also face the problem of data sparsity [27], [28]. These problems are solved by word embeddings which do not only capture syntactic but semantic information of textual data as well [29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Asim

Ghani

Ibrahim

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning-based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages. Third, for the very first time, it assesses the performance of various deep learning-based methodologies for Urdu text document classification. In this regard, for experimentation, we adapt 10 deep learning classification methodologies which have produced best performance figures for English text classification. Fourth, it also investigates the performance impact of transfer learning by utilizing Bidirectional Encoder Representations from Transformers approach for Urdu language. Fifth, it evaluates the integrity of a hybrid approach which combines traditional machine learning-based feature engineering and deep learning-based automated feature engineering. Experimental results show that feature selection approach named as normalized difference measure along with support vector machine outshines state-of-the-art performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and CLE Urdu Digest 1Million with a significant margin of 32% and 13%, respectively. Across all three datasets, normalized difference measure outperforms other filter-based feature selection algorithms as it significantly uplifts the performance of all adopted machine learning, deep learning, and hybrid approaches. The source code and presented dataset are available at Github repository https://github.com/minixain/Urdu-Text-Classification.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Asim

Ghani

Ibrahim

et al. 2020

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…The authors of the ARTM, have proposed various sets of regularizers that increase the interpretability, sparsity, and variation in the topics, produced by the model. In a series of experiments, the level of quality was achieved, comparable if not superior to the common approaches of the word2vec family [13].…”

Section: Overview Of Topic Modelling Approachesmentioning

confidence: 87%

“…∈ & ∀ ∈ ( ∪ − ) ⟹ ∈ ′ Then, the main metrics for evaluating the quality of clustering were calculated (12)(13)(14)(15):…”

Section: ∈ ⟹ ( ) ∈mentioning

confidence: 99%

Use of Topic Modelling for Improvement of Quality in the Task of Semantic Search of Educational Courses

Nikolaev¹,

Botov²,

Dmitrin³

et al. 2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

Self Cite

View full text Add to dashboard Cite

This paper proposes an approach, improving the quality of the original educational course programmes semantic search algorithm, based on vector representations, produced by distributional semantic. The proposed approach works by providing an expert with interpretable topic filtering of courses in search results. Application of probabilistic topic modeling based on additive regularization ensures the interpretability of vector components in representations of texts, allowing the expert, in the process of exploratory search, to narrow down the set of relevant documents found previously by using the vector model. In our experiments, we study the applied task of educational course search, using current requirements of the labor market (requirements described in professional standards serve as search queries). The implementation of topic filtering is based on the open-source library BigARTM. We investigate the influence of hyperparameters and the choice of regularizers in the construction of a topic model on the improvement of quality of educational course semantic search using various vector models: word2vec, fasttext, TF-IDF are investigated.

show abstract

“…As an example, Klenin and Botov [3] presented the results of the evaluation of various vector space models, namely TF-IDF, LSA, LDA, averaged Word2vec, and Paragraph2vec, within the context of educational course program documents. Paragraph2Vec, which involves using various neural networks to learn word and document vectors in low-dimensional space approaches, provides the best results for both clustering and classification tasks.…”

Section: Discussionmentioning

confidence: 99%

Exploring the power of supervised learning methods for company name disambiguation in microblog posts

Polat¹,

Çakmak²,

Nuray-Turan³

2020

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Twitter is an online social networking website where people can post short messages on any subject, and these messages become visible to other users. Users intentionally express their opinions about companies or products via microblogging texts. Analyzing such messages might help explore what customers think about company products, or what the broad feelings of customers are. Identifying tweets referring to products and companies is becoming an important tool recently. However, company names are often vague. Hence, the first step is to locate the messages that are relevant to a company. In this paper, we present a number of supervised learning techniques to decide whether a given tweet is about a company, e.g., whether a message containing the term 'amazon'is related to the company Amazon Inc. or not. Solving this task is challenging in comparison to the classical classification process. The main difficulty with this problem is that tweets and company names include limited information. To make this task tractable, external resources are used to get richer data about a company. More specifically, we generate several profiles for each organization, which contain richer information. Then we perform feature extraction to obtain both numerical and categorical features and we do feature selection to identify the most relevant attributes with our task. Finally, we train several supervised classifiers. Our constructed classifiers and carefully selected features provide high accuracy on the WePS-3 dataset. Our results show considerable improvement of accuracy by 11% over baseline approaches.

show abstract

Comparison of Vector Space Representations of Documents for the Task of Information Retrieval of Massive Open Online Courses

Cited by 5 publications

References 6 publications

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Use of Topic Modelling for Improvement of Quality in the Task of Semantic Search of Educational Courses

Exploring the power of supervised learning methods for company name disambiguation in microblog posts

Contact Info

Product

Resources

About