Morphological and Syntactic Challenges in Malayalam: A Dependency Parsing Perspective

Natural language processing is the area of study that focuses on how computers and human languages interact. Machine translation, sentiment analysis, semantic analysis, and text analysis are a few of them. The key natural language processing component is morphological analysis, which breaks words into their corresponding morphemes to determine their structure and meaning. Dependency parsing algorithms use morphological information to determine the syntactic structure of a sentence. This study evaluates the performance of various parsers, including Turbo parser, Lys-FASTPARSE, UU parser, and neural network based parser, to analyse dependency parsing methodologies used in the Malayalam language. The study evaluates the performance of these parsers in handling the difficulties and effectiveness of extensive morphological and syntactic features of Malayalam. Among these parsers, Lys-FASTPARSE performs better in LAS F1 score, MLAS score, and BLEX score, maintaining values of 56.60 and 48.58 before and after optimization. The neural network parser shows minor improvements in unlabelled attachment scores from 0.72 to 0.73 and labelled attachment scores from 0.46 to 0.47. With an LAS of 66.89% and UAS of 87.12%, the Turbo parser shows better results for baseline performance. The precision of 98.81% and recall of 88.42% in binned HEAD directions of the UU parser shows its performance in managing right direction dependencies. While lower, the parser's performance in managing left-direction and root dependencies still reflects its ability to navigate complex syntactic structures effectively. The results underscore the significance of tailored parsing techniques for morphologically rich languages like Malayalam and provide insights into optimizing parser performance for improved syntactic analysis.