Stylistic Feature Sets as Classifiers of Documents According to Their Historical Period and Ethnic Origin

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

doi:10.1080/08839514.2010.514197

Cited by 20 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Responsa project was used by HaCohen-Kerner et al [2010] to investigate the classification of documents by the ethnicity of their authors and/or to the historical period the documents were written. HaCohen-Kerner et al applied various machine-learning methods with six sets of stylistic features: quantitative, orthographic, topographic, lexical, function, and vocabulary richness.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Future research proposals that may contribute to better classification are as follows. (1) Using additional feature sets such as stylistic feature sets (HaCohen-Kerner et al, 2010B) and keyphrases that can be extracted from the text corpora (HaCohen-Kerner et al, 2007); (2) Using acronym disambiguation (e.g., HaCohen-Kerner et al, 2010A), i.e., selecting the correct long form of the acronym depending on its context will enrich the tweet's text; and (3) Using other deep learning models.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods

HaCohen-Kerner¹,

Ben-David²,

Didi³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

Self Cite

View full text Add to dashboard Cite

In this paper, we describe our submissions to SemEval-2019 task 6 contest. We tackled all three sub-tasks in this task "OffensEval-Identifying and Categorizing Offensive Language in Social Media". In our system called JCTICOL (Jerusalem College of Technology Identifies and Categorizes Offensive Language), we applied various supervised ML methods. We applied various combinations of word/character ngram features using the TF-IDF scheme. In addition, we applied various combinations of seven basic preprocessing methods. Our best submission, an RNN model was ranked at the 25 th position out of 65 submissions for the most complex sub-task (C).

show abstract

“…More ideas that may contribute to better classification are implementing TC using (1) additional feature sets such as stylistic feature sets (HaCohen-Kerner et al, 2010B) and key phrases that can be extracted from the text files (HaCohen-Kerner et al, 2007) and (…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods

HaCohen-Kerner¹,

Shayovitz²,

Rochman³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

Self Cite

View full text Add to dashboard Cite

In this paper, we describe our submissions to SemEval-2019 contest. We tackled subtask A-"a binary classification where systems have to predict whether a tweet with a given target (women or immigrants) is hateful or not hateful", a part of task 5 "Multilingual detection of hate speech against immigrants and women in Twitter (HatEval)". Our system JCTDHS (Jerusalem College of Technology Detects Hate Speech) was developed for tweets written in English. We applied various supervised ML methods, various combinations of n-gram features using the TF-IDF scheme. In addition, we applied various combinations of eight basic preprocessing methods. Our best submission was a special bidirectional RNN, which was ranked at the 11 th position out of 68 submissions.

show abstract

Stylistic Feature Sets as Classifiers of Documents According to Their Historical Period and Ethnic Origin

Cited by 20 publications

References 21 publications

Deep Learning for Period Classification of Historical Hebrew Texts

Deep Learning for Period Classification of Historical Hebrew Texts

JCTICOL at SemEval-2019 Task 6: Classifying Offensive Language in Social Media using Deep Learning Methods, Word/Character N-gram Features, and Preprocessing Methods

JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods

Contact Info

Product

Resources

About