Text classification in the Turkish marketing domain for context sensitive ad distribution

Engin, Melih; Can, Tolga

doi:10.1109/iscis.2009.5291861

Cited by 1 publication

(2 citation statements)

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“…67,68 As one of the most fundamental tasks in natural language processing (NLP), text classification has been widely studied. 69 Examples of setups and applications are (but not limited to) social media, 70 healthcare, [71][72][73] information retrieval, 74 sentiment analysis, [75][76][77][78][79] content-based recommender systems, 80 document summarization, 81,82 various business and marketing applications, [83][84][85] and legal document categorization. 86 A variety of languages were targeted over time for the popular text classification task, including well-studied languages, such as Arabic, 87,88 Turkish, 83,[89][90][91] French, 71,92 Spanish, 72 and Indian, 93 as well as underresourced languages, such as Romanian.…”

Section: Text Classificationmentioning

confidence: 99%

“…69 Examples of setups and applications are (but not limited to) social media, 70 healthcare, [71][72][73] information retrieval, 74 sentiment analysis, [75][76][77][78][79] content-based recommender systems, 80 document summarization, 81,82 various business and marketing applications, [83][84][85] and legal document categorization. 86 A variety of languages were targeted over time for the popular text classification task, including well-studied languages, such as Arabic, 87,88 Turkish, 83,[89][90][91] French, 71,92 Spanish, 72 and Indian, 93 as well as underresourced languages, such as Romanian. 94 The applied classification techniques range from shallow methods, such as Logistic Regression, 95 SVM, 96 and Naïve Bayes, 97 to more complex and resource-hungry deep neural networks, such as CNNs, 62,98 Hierarchical Attention Networks (HANs), 99 and the powerful transformer-based methods that started to dominate the landscape in recent years.…”

Section: Text Classificationmentioning

confidence: 99%

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The unreasonable effectiveness of machine learning in Moldavian versus Romanian dialect identification

Găman

Ionescu

2021

Int J of Intelligent Sys

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Motivated by the seemingly high accuracy levels of machine learning (ML) models in Moldavian versus Romanian dialect identification and the increasing research interest on this topic, we provide a follow-up on the Moldavian versus Romanian Cross-Dialect Topic Identification (MRC) shared task of the VarDial 2019 evaluation campaign. The shared task included two subtask types: one that consisted in discriminating between the Moldavian and Romanian dialects and one that consisted in classifying documents by topic across the two dialects of Romanian. Participants achieved impressive scores, for example, the top model for Moldavian versus Romanian dialect identification obtained a macro-F 1 score of 0.895. We conduct a subjective evaluation by human annotators, showing that humans attain much lower accuracy rates compared with ML models. Hence, it remains unclear why the methods proposed by participants attain such high accuracy rates. Our goal is to understand (i) why the proposed methods work so well (by visualizing the discriminative features) and (ii) to what extent these methods can keep their high accuracy levels, for example, when we shorten the text samples to single sentences or when we use tweets at inference time. A secondary goal of our

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