Mining and Using Key-Words and Key-Phrases to Identify the Era of an Anonymous Text

Mughaz, Dror; HaCohen-Kerner, Yaakov; Gabbay, Dov M.

doi:10.1007/978-3-319-59268-8_6

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…In this work, we search for keyphrases or keywords that appear close to citations in pre-modern Hebrew-Aramaic texts, and extract TREs from this set. One of the essential differences between citations in these texts and modern texts, especially scientific ones, is that there are no special patterns representing references [57], and no bibliography.…”

Section: Related Workmentioning

confidence: 99%

Extraction of time-related expressions using text mining with application to Hebrew

Mughaz,

HaCohen-Kerner,

Gabbay

2024

PLoS ONE

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In this research, we extract time-related expressions from a rabbinic text in a semi-automatic manner. These expressions usually appear next to rabbinic references (name / nickname / acronym / book-name). The first step toward our goal is to find all the expressions near references in the corpus. However, not all of the phrases around the references are time-related expressions. Therefore, these phrases are initially considered to be potential time-related expressions. To extract the time-related expressions, we formulate two new statistical functions, and we use screening and heuristic methods. We tested these statistical functions, grammatical screenings, and heuristic methods on a corpus containing responsa documents. In this corpus, many rabbinic citations are known and marked. The statistical functions and the screening methods filtered the potential time-related expressions and reduced 99.88% of the initial expressions (from 484,681 to 575).

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Section: Related Workmentioning

confidence: 99%

Extraction of time-related expressions using text mining with application to Hebrew

Mughaz,

HaCohen-Kerner,

Gabbay

2024

PLoS ONE

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“…Liebeskind et al also classified the candidate terms into two historical periods: ancient (from the 11 th century until the 19 th century) and modern (from the 20 th century). Mughaz et al [2015Mughaz et al [ , 2017 investigated how to determine the time-frame a given Responsa author lived. Mughaz et al [2017] defined a set of key phrases and formulated various rules: iron clad, heuristic, and greedy.…”

Section: The Responsa Corpus and Diachronic Tasksmentioning

confidence: 99%

“…Mughaz et al [2015Mughaz et al [ , 2017 investigated how to determine the time-frame a given Responsa author lived. Mughaz et al [2017] defined a set of key phrases and formulated various rules: iron clad, heuristic, and greedy. Their corpus contains 15,495 files written by 24 rabbis, averaging 643 files for each author.…”

Section: The Responsa Corpus and Diachronic Tasksmentioning

confidence: 99%

Deep Learning for Period Classification of Historical Hebrew Texts

Liebeskind

Liebeskind²

2020

Journal of Data Mining &Amp; Digital Humanities

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In this study, we address the interesting task of classifying historical texts by their assumed period of writ-ing. This task is useful in digital humanity studies where many texts have unidentified publication dates.For years, the typical approach for temporal text classification was supervised using machine-learningalgorithms. These algorithms require careful feature engineering and considerable domain expertise todesign a feature extractor to transform the raw text into a feature vector from which the classifier couldlearn to classify any unseen valid input. Recently, deep learning has produced extremely promising re-sults for various tasks in natural language processing (NLP). The primary advantage of deep learning isthat human engineers did not design the feature layers, but the features were extrapolated from data witha general-purpose learning procedure. We investigated deep learning models for period classification ofhistorical texts. We compared three common models: paragraph vectors, convolutional neural networks (CNN) and recurrent neural networks (RNN), and conventional machine-learning methods. We demon-strate that the CNN and RNN models outperformed the paragraph vector model and the conventionalsupervised machine-learning algorithms. In addition, we constructed word embeddings for each timeperiod and analyzed semantic changes of word meanings over time.

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“…They presented and applied a semi-automatic method that facilitates the extraction of time-related key-phrases. In other works of Mughaz et al (2014aMughaz et al ( , 2014bMughaz et al ( , 2017Mughaz et al ( , 2019aMughaz et al ( , 2019b and HaCohen-Kerner & Mughaz (2010) they improved upon the previous method and used time-related phrases and references in order to date texts. The dating they suggested could help identify ancient anonymous texts and could even help identify edited texts.…”

Section: Related Workmentioning

confidence: 99%

From an Artificial Neural Network to Teaching

Mughaz

Cohen

Mejahez³

et al. 2020

IJELL

Self Cite

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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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Mining and Using Key-Words and Key-Phrases to Identify the Era of an Anonymous Text

Cited by 5 publications

References 17 publications

Extraction of time-related expressions using text mining with application to Hebrew

Extraction of time-related expressions using text mining with application to Hebrew

Deep Learning for Period Classification of Historical Hebrew Texts

From an Artificial Neural Network to Teaching

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