![]() ![]() Statistical approach is more accurate and widely used, and there are several libraries and tools available to perform POS tagging. In conclusion, Part-of-Speech tagging is a technique that assigns grammatical category to words in a text, which is important for natural language processing tasks. It is a crucial step in understanding the meaning of text, as the POS tags provide important information about the syntactic structure of a sentence. POS tagging is an important step in many NLP tasks, and it is used as a pre-processing step for other NLP tasks such as named entity recognition, sentiment analysis, and text summarization. In addition to NLTK, other popular POS tagging tools include the Stanford POS Tagger, the OpenNLP POS Tagger, and the spaCy library. NLTK also includes a pre-trained POS tagger based on the Penn Treebank POS tag set, which is a widely used standard for POS tagging. One of the most popular POS tagging tools is the Natural Language Toolkit (NLTK) library in Python, which provides a set of functions for tokenizing, POS tagging, and parsing text. The most common machine learning algorithm used for POS tagging is the Hidden Markov Model (HMM), which uses a set of states and transition probabilities to predict the POS tag of a word. Statistical POS tagging is more accurate and widely used because it can take into account the context in which a word is used and learn from a large corpus of annotated text. Rule-based tagging uses a set of hand-written rules to assign POS tags to words, while statistical tagging uses machine learning algorithms to learn the POS tag of a word based on its context. Here tagging done with the probability of the occurrence of the sentence structure along with the dictionary entry.There are two main approaches to POS tagging: rule-based and statistical. This algorithm use Multithreaded Technology. My proposed Approach use Dictionary entries along with adjacent tag information. So it affects the accuracy of the result of Malayalam POS Tagging. The currently used Algorithms are efficient Machine Learning Algorithms but these are not built for Malayalam. Malayalam is a Dravidian family of languages, inflectional with suffixes with the root word forms. Stochastic Approach is the widely used one nowadays because of its accuracy. It use large corpus, so that Time complexity and Space complexity is high whereas Rule base approach has less complexity for both Time and Space. Stochastic Approach use probabilistic and statistical information to assign tag to words. This is the oldest approach and it use lexicon or dictionary for reference. Rule based Approach use predefined handwritten rules. Those are Rule based Approach and Stochastic Approach. There are mainly two approaches usually followed in Parts of Speech Tagging. A large number of current language processing systems use a parts-of-speech tagger for pre-processing. Usually, these tags indicate syntactic classification like noun or verb, and sometimes include additional information, with case markers (number, gender etc) and tense markers. ![]() A tag mentions the word’s usage in the sentence. Parts-of-speech tagging is the process of labeling each word in a sentence. ![]()
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