Words as Classifiers of Documents According to Their Historical Period and the Ethnic Origin of Their Authors

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

doi:10.1080/01969720801944299

Cited by 16 publications

(4 citation statements)

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“…For instance, Forman (2003), in his study on feature selection metrics for TC, claimed that stop words are ambiguous and therefore should be removed. However, HaCohen-Kerner et al (2008) demonstrated that the use of word unigrams including stop words lead to improved TC results compared to the results obtained using word unigrams excluding stop words. In our system, we applied various combinations of preprocessing types depending on the language.…”

Section: Text Preprocessingmentioning

confidence: 96%

JCT at SemEval-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams

Uzan¹,

HaCohen-Kerner

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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In this paper, we describe our submissions to SemEval-2020 contest. We tackled subtask 12 -"Multilingual Offensive Language Identification in Social Media". We developed different models for four languages: Arabic, Danish, Greek, and Turkish. We applied three supervised machine learning methods using various combinations of character and word n-gram features. In addition, we applied various combinations of basic preprocessing methods. Our best submission was a model we built for offensive language identification in Danish using Random Forest. This model was ranked at the 6 th position out of 39 submissions. Our result is lower by only 0.0025 than the result of the team that won the 4 th place using entirely non-neural methods. Our experiments indicate that char ngram features are more helpful than word ngram features. This phenomenon probably occurs because tweets are more characterized by characters than by words, tweets are short, and contain various special sequences of characters, e.g., hashtags, shortcuts, slang words, and typos.

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Section: Text Preprocessingmentioning

confidence: 96%

JCT at SemEval-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams

Uzan¹,

HaCohen-Kerner

2020

Proceedings of the Fourteenth Workshop on Semantic Evaluation

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“…Most of the studies use a relatively small number of the following components: datasets, ML methods, preprocessing methods, and combinations of these. Moreover, portions of the conclusions of these studies seemingly contradict each other (e.g., stopword removal improves classification accuracy [15,17,21] or does not improve classification accuracy [16,31,37]). Table 1 summarizes the attributes of twelve studies described in this section that addressed preprocessing for TC.…”

Section: Plos Onementioning

confidence: 99%

The influence of preprocessing on text classification using a bag-of-words representation

2020

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Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text preprocessing, e.g., conversion of uppercase letters into lowercase letters, HTML tag removal, stopword removal, punctuation mark removal, lemmatization, correction of common misspelled words, and reduction of replicated characters. We hypothesize that the application of different combinations of preprocessing methods can improve TC results. Therefore, we performed an extensive and systematic set of TC experiments (and this is our main research contribution) to explore the impact of all possible combinations of five/ six basic preprocessing methods on four benchmark text corpora (and not samples of them) using three ML methods and training and test sets. The general conclusion (at least for the datasets verified) is that it is always advisable to perform an extensive and systematic variety of preprocessing methods combined with TC experiments because it contributes to improve TC accuracy. For all the tested datasets, there was always at least one combination of basic preprocessing methods that could be recommended to significantly improve the TC using a BOW representation. For three datasets, stopword removal was the only single preprocessing method that enabled a significant improvement compared to the baseline result using a bag of 1,000-word unigrams. For some of the datasets, there was minimal improvement when we removed HTML tags, performed spelling correction or removed punctuation marks, and reduced replicated characters. However, for the fourth dataset, the stopword removal was not beneficial. Instead, the conversion of uppercase letters into lowercase letters was the only single preprocessing method that demonstrated a significant improvement compared to the baseline result. The best result for this dataset was obtained when we performed spelling correction and conversion into lowercase letters. In general, for all the datasets processed, there was always at least one combination of basic preprocessing methods that could be recommended to improve the accuracy results when using a bag-of-words representation.

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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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Words as Classifiers of Documents According to Their Historical Period and the Ethnic Origin of Their Authors

Cited by 16 publications

References 10 publications

JCT at SemEval-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams

JCT at SemEval-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams

The influence of preprocessing on text classification using a bag-of-words representation

From an Artificial Neural Network to Teaching

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