POS TAGGING USING CONDITIIONAL RANDOM FIELD

POS tagging involves assigning a grammatical category, such as noun, verb, or adjective, to each word in a sentence, which is essential for understanding and processing linguistic structures. A CRF is a sequence modeling algorithm which is used to identify entities or patterns in text, such as POS tags. Many algorithms have been proposed for tagging the information with necessary tags. Conditional Random Field is a Classification technique used for POS tagging. To improve the efficiency of the Conditional Random Field algorithm, Long Short Term Memory is used at one of the hidden layer of the Conditional Random Field. with this method good accuracy achieved when compare with these two CRF and LSTM Individually. CRFs are trained using maximum likelihood estimation, which involves optimizing the parameters of the model to maximize the probability of the correct output sequence given the input features. Algorithms used are:Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS): A popular choice for training CRFs due to its efficiency in handling the large number of parameters and the large-scale data typical in NLP tasks. It approximates the inverse Hessian to perform optimization. L-BFGS with Line Search: Enhances L-BFGS by performing a line search to find an optimal step size during each iteration, improving convergence. Viterbi algorithm performs a step-by-step computation to find the most likely sequence of POS tags for the given input sequence. It maintains a matrix where each cell represents the maximum score of a particular tag at a particular position, taking into account both the previous tag and the current word.