What is it about?
In the realm of Natural Language Processing (NLP), text pre-processing, particularly for informal content from social media, necessitates advanced word-level tokenization techniques to refine data by eliminating stopwords and irrelevant characters. The effective tokenization of Urdu, a language with scarce computational resources, is impeded by traditional methods that fail to account for its linguistic nuances, adversely affecting critical NLP tasks such as aspect mining and Named Entity Recognition (NER). Addressing these challenges, we propose an enhanced pre-processing strategy for Urdu, incorporating outlier detection via Inter-Quartile Range (IQR) and normalization algorithms to augment tokenization efficacy. Through the analysis of a substantial corpus of Urdu tweets, our approach demonstrated significant improvements in token distribution and accuracy, particularly when paired with the Urduhack tokenizer, indicating superior topic modelling outcomes. The comparative assessment using NMF and LDA models revealed the superiority of our method, particularly in coherence and precision, when utilizing bigram features for topic extraction.
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Why is it important?
We have proposed a novel framework that improves the quality of the tokens generated by tokenizers, seeking to eliminate erroneous tokens (outliers), thereby enhancing the inclusion of authentic words in the language processing pipeline.
Perspectives
The significance of this research within the domain of the Urdu language, classified as a low-resource language, is manifold. It introduces a specialized pre-processing technique tailored to address the intricacies of Urdu text processing, which is often plagued by the lack of sophisticated tools and resources. By implementing novel outlier detection and normalization methodologies, the study significantly enhances the quality and reliability of Urdu text tokenization, which is a foundational step for further language processing tasks. The development of such advanced processing capabilities is crucial for improving the performance of Urdu in various NLP applications, including but not limited to topic modelling, sentiment analysis, and entity recognition. Consequently, this work represents a substantial progression in the computational handling of Urdu, facilitating more nuanced and accurate language models that can effectively manage and interpret the rich linguistic features of Urdu.
Seemab Latif
National University of Sciences and Technology
Read the Original
This page is a summary of: Assessing Urdu Language Processing Tools via Statistical and Outlier Detection Methods on Urdu Tweets, ACM Transactions on Asian and Low-Resource Language Information Processing, September 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3622939.
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