Don't Use a Lot When Little Will Do: Genre Identification Using URLs

Priyatam, Pattisapu Nikhil; Iyengar, Srinivasan; Perumal, K.; Varma, Vasudeva

doi:10.13053/rcs-70-1-18

Cited by 3 publications

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Automatic genre identification: a survey

Kuzman,

Ljubešić

2023

Lang Resources & Evaluation

View full text Add to dashboard Cite

Automatic genre identification (AGI) is a text classification task focused on genres, i.e., text categories defined by the author’s purpose, common function of the text, and the text’s conventional form. Obtaining genre information has been shown to be beneficial for a wide range of disciplines, including linguistics, corpus linguistics, computational linguistics, natural language processing, information retrieval and information security. Consequently, in the past 20 years, numerous researchers have collected genre datasets with the aim to develop an efficient genre classifier. However, their approaches to the definition of genre schemata, data collection and manual annotation vary substantially, resulting in significantly different datasets. As most AGI experiments are dataset-dependent, a sufficient understanding of the differences between the available genre datasets is of great importance for the researchers venturing into this area. In this paper, we present a detailed overview of different approaches to each of the steps of the AGI task, from the definition of the genre concept and the genre schema, to the dataset collection and annotation methods, and, finally, to machine learning strategies. Special focus is dedicated to the description of the most relevant genre schemata and datasets, and details on the availability of all of the datasets are provided. In addition, the paper presents the recent advances in machine learning approaches to automatic genre identification, and concludes with proposing the directions towards developing a stable multilingual genre classifier.

show abstract

Automatic genre identification: a survey

Kuzman,

Ljubešić

2023

Lang Resources & Evaluation

View full text Add to dashboard Cite

show abstract

Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native

Arian

Abutbul²,

Aharon³

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models

Kuzman,

Mozetič,

Ljubešić

2023

MAKE

View full text Add to dashboard Cite

Massive text collections are the backbone of large language models, the main ingredient of the current significant progress in artificial intelligence. However, as these collections are mostly collected using automatic methods, researchers have few insights into what types of texts they consist of. Automatic genre identification is a text classification task that enriches texts with genre labels, such as promotional and legal, providing meaningful insights into the composition of these large text collections. In this paper, we evaluate machine learning approaches for the genre identification task based on their generalizability across different datasets to assess which model is the most suitable for the downstream task of enriching large web corpora with genre information. We train and test multiple fine-tuned BERT-like Transformer-based models and show that merging different genre-annotated datasets yields superior results. Moreover, we explore the zero-shot capabilities of large GPT Transformer models in this task and discuss the advantages and disadvantages of the zero-shot approach. We also publish the best-performing fine-tuned model that enables automatic genre annotation in multiple languages. In addition, to promote further research in this area, we plan to share, upon request, a new benchmark for automatic genre annotation, ensuring the non-exposure of the latest large language models.

show abstract

Don't Use a Lot When Little Will Do: Genre Identification Using URLs

Cited by 3 publications

References 10 publications

Automatic genre identification: a survey

Automatic genre identification: a survey

Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native

Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models

Contact Info

Product

Resources

About