Cuisine: Classification using stylistic feature sets and/or name‐based feature sets

HaCohen-Kerner, Yaakov; Beck, Hananya; Yehudai, Elchai; Rosenstein, Mordechay; Mughaz, Dror

doi:10.1002/asi.21350

Cited by 16 publications

(4 citation statements)

References 43 publications

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“…To overcome this combinatorial explosion for non-small values of n, several variants of hill-climbing have been proposed, (e.g., [68]). An application of TC to hill-climbing using feature sets was successfully demonstrated in HaCohen-Kerner et al [69].…”

Section: B Feature Sets and A Hill-climbing Modelmentioning

confidence: 99%

Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method

et al. 2022

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In this study, we aim to detect in social media texts written in Hebrew girls who are suspected of being anorexic. We constructed a dataset containing 100 blog posts written by females who are probably anorexic, and 100 blog posts written by females who are likely to be non-anorexic. The construction of this dataset was supervised and approved by an international expert on anorexia. We tested several text classification (TC) methods, using various feature sets (content-based and style-based), five machine learning (ML) methods, three RNN models, four BERT models, three basic preprocessing methods, three feature filtering methods, and parameter tuning. Several insights were found as follows. A set of 50-word n-grams (mostly word unigrams) given by an expert was found as a good basic detector. A heuristic process based on the random forest ML method has overcome a combinatorial explosion and led to significant improvement over a baseline result at a level of P = .01. Application of an iterative process that tests combinations of ''k out of n '' where n < n (n is the number of feature sets) lead to a result of 90.63%, using a combination of 300 features from ten feature sets.

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Section: B Feature Sets and A Hill-climbing Modelmentioning

confidence: 99%

Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method

et al. 2022

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“…Other studies that are related to document classification and address the challenges of Hebrew involve the classification of Hebrew-Aramaic documents according to style (Koppel et al, 2006;Mughaz, 2003); authorship verification, including forgeries and pseudonyms (Koppel et al, 2003(Koppel et al, , 2004 and classification of texts according to their ethnic origin and their historical period (HaCohen-Kerner, Beck, Yehudai & Mughaz, 2006;HaCohen-Kerner, Mughaz et al, 2008;HaCohen-Kerner, Beck, Yehudai, Rosenstein & Mughaz, 2010).…”

Section: Related Workmentioning

confidence: 99%

From an Artificial Neural Network to Teaching

Mughaz

Cohen

Mejahez³

et al. 2020

IJELL

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Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student’s grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence learning processes, with and without a general overview of the problem that it is under examination. Applying the idea of the general perspective that the network gets on the sentences and deriving recommendations from this for teaching processes. Background: We looked for common patterns of computer errors and human grammar mistakes. Also deducing the neural network’s learning process, deriving conclusions, and applying concepts from this process to the process of human learning. Methodology: We used DL technologies and research methods. After analysis, we built models from three types of complex neuronal networks – LSTM, Bi-LSTM, and GRU – with sequence-to-sequence architecture. After this, we combined the sequence-to- sequence architecture model with the attention mechanism that gives a general overview of the input that the network receives. Contribution: The cost of computer applications is cheaper than that of manual human effort, and the availability of a computer program is much greater than that of humans to perform the same task. Thus, using computer applications, we can get many desired examples of mistakes without having to pay humans to perform the same task. Understanding the mistakes of the machine can help us to under-stand the human mistakes, because the human brain is the model of the artificial neural network. This way, we can facilitate the student learning process by teaching students not to make mistakes that we have seen made by the artificial neural network. We hope that with the method we have developed, it will be easier for teachers to discover common mistakes in students’ work before starting to teach them. In addition, we show that a “general explanation” of the issue under study can help the teaching and learning process. Findings: We performed the test case on the Hebrew language. From the mistakes we received from the computerized neuronal networks model we built, we were able to classify common human errors. That is, we were able to find a correspondence between machine mistakes and student mistakes. Recommendations for Practitioners: Use an artificial neural network to discover mistakes, and teach students not to make those mistakes. We recommend that before the teacher begins teaching a new topic, he or she gives a general explanation of the problems this topic deals with, and how to solve them. Recommendations for Researchers: To use machines that simulate the learning processes of the human brain, and study if we can thus learn about human learning processes. Impact on Society: When the computer makes the same mistakes as a human would, it is very easy to learn from those mistakes and improve the study process. The fact that ma-chine and humans make similar mistakes is a valuable insight, especially in the field of education, Since we can generate and analyze computer system errors instead of doing a survey of humans (who make mistakes similar to those of the machine); the teaching process becomes cheaper and more efficient. Future Research: We plan to create an automatic grammar-mistakes maker (for instance, by giving the artificial neural network only a tiny data-set to learn from) and ask the students to correct the errors made. In this way, the students will practice on the material in a focused manner. We plan to apply these techniques to other education subfields and, also, to non-educational fields. As far as we know, this is the first study to go in this direction ‒ instead of looking at organisms and building machines, to look at machines and learn about organisms.

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“…The experimental analysis indicated that the presented scheme outperforms representation based on the distributional lexical measures, such as functions of vocabulary richness and frequencies of the occurrence of the most frequent words. HaCohen-Kerner et al [23] also utilised stylistic feature sets and name-based feature sets for text classification. Lee and Myaeng [10] presented a genre classification methodology based on document frequency ratios across genres and across subject classes.…”

Section: Literature Reviewmentioning

confidence: 99%

An ensemble scheme based on language function analysis and feature engineering for text genre classification

Onan

2016

Journal of Information Science

254

119

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Text genre classification is the process of identifying functional characteristics of text documents. The immense quantity of text documents available on the web can be properly filtered, organised and retrieved with the use of text genre classification, which may have potential use on several other tasks of natural language processing and information retrieval. Genre may refer to several aspects of text documents, such as function and purpose. The language function analysis (LFA) concentrates on single aspect of genres and it aims to classify text documents into three abstract classes, such as expressive, appellative and informative. Text genre classification is typically performed by supervised machine learning algorithms. The extraction of an efficient feature set to represent text documents is an essential task for building a robust classification scheme with high predictive performance. In addition, ensemble learning, which combines the outputs of individual classifiers to obtain a robust classification scheme, is a promising research field in machine learning research. In this regard, this article presents an extensive comparative analysis of different feature engineering schemes (such as features used in authorship attribution, linguistic features, character n-grams, part of speech n-grams and the frequency of the most discriminative words) and five different base learners (Naïve Bayes, support vector machines, logistic regression, k-nearest neighbour and Random Forest) in conjunction with ensemble learning methods (such as Boosting, Bagging and Random Subspace). Based on the empirical analysis, an ensemble classification scheme is presented, which integrates Random Subspace ensemble of Random Forest with four types of features (features used in authorship attribution, character n-grams, part of speech n-grams and the frequency of the most discriminative words). For LFA corpus, the highest average predictive performance obtained by the proposed scheme is 94.43%.

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Cuisine: Classification using stylistic feature sets and/or name‐based feature sets

Cited by 16 publications

References 43 publications

Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method

Detection of Anorexic Girls-In Blog Posts Written in Hebrew Using a Combined Heuristic AI and NLP Method

From an Artificial Neural Network to Teaching

An ensemble scheme based on language function analysis and feature engineering for text genre classification

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