An efficiency dependency parser using hybrid approach for tamil language
This work addresses the need for more accurate and scalable dependency parsers in Tamil natural language processing, particularly for applications like machine translation, though it is incremental as it builds on existing methods.
The paper tackles the problem of dependency parsing for Tamil language by proposing a hybrid approach that combines rule-based and machine learning methods to identify structural relationships between words in sentences, resulting in a tool that improves accuracy and handles large data volumes.
Natural language processing is a prompt research area across the country. Parsing is one of the very crucial tool in language analysis system which aims to forecast the structural relationship among the words in a given sentence. Many researchers have already developed so many language tools but the accuracy is not meet out the human expectation level, thus the research is still exists. Machine translation is one of the major application area under Natural Language Processing. While translation between one language to another language, the structure identification of a sentence play a key role. This paper introduces the hybrid way to solve the identification of relationship among the given words in a sentence. In existing system is implemented using rule based approach, which is not suited in huge amount of data. The machine learning approaches is suitable for handle larger amount of data and also to get better accuracy via learning and training the system. The proposed approach takes a Tamil sentence as an input and produce the result of a dependency relation as a tree like structure using hybrid approach. This proposed tool is very helpful for researchers and act as an odd-on improve the quality of existing approaches.