Identifying Historical Period and Ethnic Origin of Documents Using Stylistic Feature Sets

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

doi:10.1007/11893318_13

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…The research described in this article is clearly developed and expanded beyond the conference paper presented by us in HaCohen‐Kerner et al (2006), as follows: (a) The background and the related researches in various subdomains were enlarged significantly; (b) new feature sets (name‐based feature sets) have been defined and applied; (c) additional classification experiments were applied to also identify places where the responsa were written; (d) instead of applying the five‐fold cross‐validation, we apply the 10‐fold cross‐validation. The 10‐fold cross‐validation tends to give a less biased estimate of true generalization error.…”

Section: The Modelmentioning

confidence: 80%

“…In this research, we presented CUISINE, which extends and improves the system presented in HaCohen‐Kerner et al (2006) by: (a) new feature sets (name‐based feature sets); (b) new applications such as places where the responsa were written and a total of seven different experiments instead of three, and (c) better results: CUISINE achieved accuracy results of 98.99, 94.08, and 90.71% for the following classification experiments: ethnicity, ethnicity&time, and ethnicity&time&place, respectively, compared to those achieved by the previous system: 98.67 and 92.81% for ethnicity and ethnicity&time, respectively.…”

Section: Discussionmentioning

confidence: 94%

“…In an earlier conference paper (HaCohen‐Kerner, Beck, Yehudai, & Mughaz, 2006), we addressed the identification of historical period and/or ethnic origin of documents based on stylistic feature sets using the Support Vector Machines (SVM) ML method. The classification accuracy results of the three examined classification tasks (ethnic origin, historical period, ethnic origin and historical period) were 98.67, 98.95, and 92.81%, respectively.…”

Section: Hebrew–aramaic Texts and Their Classificationmentioning

confidence: 99%

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

HaCohen-Kerner

Beck

Yehudai

et al. 2010

J. Am. Soc. Inf. Sci.

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In contrast, the name-based feature sets are rather poor for these tasks. However, for the most complex task (ethnicity&time&place), a hill-climbing model using all feature sets succeeds in significantly improving the classification results. Most of the stylistic features (34 of 42) are language-independent and domain-independent. These features might be useful to the community at large, at least for rather simple tasks.

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Section: The Modelmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 94%

Section: Hebrew–aramaic Texts and Their Classificationmentioning

confidence: 99%

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

HaCohen-Kerner

Beck

Yehudai

et al. 2010

J. Am. Soc. Inf. Sci.

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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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“…Another works that are related to document classification and address the challenges of Hebrew involve the classification of Hebrew-Aramaic documents according to style (Koppel, Mughaz, & Akiva, 2006;Mughaz, 2003); authorship verification, including forgers and pseudonyms (Koppel, Mughaz, & Akiva, 2003;Koppel, Schler, & Mughaz, 2004); and classification of texts according to their ethnic origin and their historical period (HaCohen-Kerner, Beck, Yehudai & Mughaz, 2006;…”

Section: Related Workmentioning

confidence: 99%

Extracting and Tagging Unstructured Citation of a Hebrew Religious Document

Mughaz¹,

HaCohen-Kerner²,

Gabbay³

2019

InSITE Conference

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Aim/Purpose: Finding and tagging citation on an ancient Hebrew religious document. These documents have no structured citations and have no bibliography. Background: We look for common patterns within Hebrew religious texts. Methodology: We developed a method that goes over the texts and extracts sentences con-taining the names of three famous authors. Within these sentences we find common ways of addressing those three authors and with these patterns we find references to various other authors. Contribution: This type of text is rich in citations and references to authors, but because there is no structure of references it is very difficult for a computer to automatically identify the references. We hope that with the method we have developed it will be easier for a computer to identify references and even turn them into hyper-links. Findings: We have provided an algorithm to solve the problem of non-structured cita-tions in an old Hebrew plain text. The algorithm definitely was able to find many citations but it has missed out some types of citations. Impact on Society: When the computer recognizes references, it will be able to build (at least par-tially) a bibliography that currently does not exist in such texts at all. Over time, OCR scans more and more ancient texts. This method can make people's access and understanding much. Future Research: After we identify the references, we plan to automatically create a bibliography for these texts and even transform those references into hyperlinks.

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Identifying Historical Period and Ethnic Origin of Documents Using Stylistic Feature Sets

Cited by 4 publications

References 22 publications

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

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

From an Artificial Neural Network to Teaching

Extracting and Tagging Unstructured Citation of a Hebrew Religious Document

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