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引用次数: 0
摘要
自然语言处理(NLP)领域的最新进展提高了执行任务的系统性能,如在英语和中文等资源丰富的语言中执行情感检测(ED)、信息检索、翻译等任务。但由于缺乏注释数据集,马拉雅拉姆语还没有取得类似的进步。由于马拉雅拉姆语具有丰富的词形、自由词序和聚合特征,因此数据准备工作极具挑战性。在本文中,我们采用了传统的机器学习(ML)技术,如支持向量机(SVM)和多层感知器(MLP),以及最新的深度学习方法,如递归神经网络(RNN)和先进的基于变换器的方法来训练情绪检测系统。这项工作非常突出,因为之前从马拉雅拉姆语文本中提取情感的所有尝试都依赖于词典,而词典并不适合处理大量数据。通过调整超参数,我们增强了名为 MuRIL 的基于变换器的模型,从而获得了 79% 的准确率,并将其与唯一的最先进模型(SOTA)进行了比较。我们发现,所提出的技术超越了迄今为止所报道的用于检测马拉雅拉姆语情绪的 SOTA 系统。
Emotion Detection System for Malayalam Text using Deep Learning and Transformers
Recent advances in Natural Language Processing (NLP) have improved the performance of the systems that perform tasks, such as Emotion Detection (ED), Information Retrieval, Translation, etc., in resource-rich languages like English and Chinese. But similar advancements have not been made in Malayalam due to the dearth of annotated datasets. Because of its rich morphology, free word order and agglutinative character, data preparation in Malayalam is highly challenging. In this paper, we employ traditional Machine Learning (ML) techniques such as support vector machines (SVM) and multilayer perceptrons (MLP), and recent deep learning methods such as Recurrent Neural Networks (RNN) and advanced transformer-based methodologies to train an emotion detection system. This work stands out since all the previous attempts to extract emotions from Malayalam text have relied on lexicons, which are inappropriate for handling large amounts of data. By tweaking the hyperparameters, we enhanced the transformer-based model known as MuRIL to obtain an accuracy of 79%, which is then compared with the only state-of-the-art (SOTA) model. We found that the proposed techniques surpass the SOTA system available for detecting emotions in Malayalam reported so far.
期刊介绍:
The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to:
-Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc.
-Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc.
-Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition.
-Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc.
-Machine Translation involving Asian or low-resource languages.
-Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc.
-Information Extraction and Filtering: including automatic abstraction, user profiling, etc.
-Speech processing: including text-to-speech synthesis and automatic speech recognition.
-Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc.
-Cross-lingual information processing involving Asian or low-resource languages.
-Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.