MARKOV MODELS: OVERVIEW Morkov models extract linguistic knowledge automatically from the large corpora and do POS tagging. Morkov models are alternatives for laborious and time-consuming manual tagging. MARKOV PROPERTY The name Markov model is derived from the term Markov property. Markov property is an assumption that allows the system to be analyzed. According to Markov property, […]
What is Transformation-Based Learning? Transformation-based learning (TBL) is a rule-based algorithm for automatic tagging of parts-of-speech to the given text. TBL transforms one state to another using transformation rules in order to find the suitable tag for each word. TBL allows us to have linguistic knowledge in a readable form. It extracts linguistic information automatically
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MAXIMUM ENTROPY TAGGING Maximum Entropy Tagging aims to find a model with maximum entropy. The term, maximum entropy here means maximum randomness or minimum additional structure. It exploits some of the good properties of tranformation-based learning and Markov model tagging. It allows flexibility in cues used to disambiguate words. The outputs of the maximum entropy tagging are tags
WHAT IS TAGGING? Automatic assignment of descriptors to the given tokens is called Tagging. The descriptor is called tag. The tag may indicate one of the parts-of-speech, semantic information, and so on. So tagging a kind of classification. WHAT IS PARTS-OF-SPEECH TAGGING? The process of assigning one of the parts of speech to the given
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RULE-BASED PARTS-OF-SPEECH TAGGING Rule-based part-of-speech tagging is the oldest approach that uses hand-written rules for tagging. Rule based taggers depends on dictionary or lexicon to get possible tags for each word to be tagged. Hand-written rules are used to identify the correct tag when a word has more than one possible tag. Disambiguation is done
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Baum-Welch Algorithm, also known as forward-backword algorithm was invented by Leonard E. Baum and Lloyd R Welch. It is a special case of Estimation Maximization (EM) method. Baum-Welch algorithm is very effective to train a Markov model without using manually annotated corpora. Baum Welch algorithm works by assigning initial probabilities to all the parameters. Then until the training converges,
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BOOKS FOR REFERENCE Ruslan Mitkov, The Oxford Handbook Of Computational Linguistics, Oxford Universitty Press, 2003. Robert Dale, Hermani Moisi, Harold Somers, Handbook Of Natural Language Processing, Markcel Dekker Inc. James Allen, Natural Language Processing, Pearson Education, 2003. Christopher D.Manning & Henrich Schutze, Foundations Of Statistical Natural Language Processing, The MIT Press, 2001 Douglas Biber, Susan
Tamil is one of the classical languages spoken by more than 77 million people across the world. Tamil language is a member of the south Dravidian family. It is one of the most ancient languages still spoken in the world. It is written in south Brahmi script. Tamil has 12 vowels and 18 consonants. Tamil
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This article presents an overview of Tokenization and the challenges associated with it. WHAT IS TOKENIZATION? Tokenization is the process of breaking up the given text into units called tokens. The tokens may be words or number or punctuation mark. Tokenization does this task by locating word boundaries. Ending point of a word and beginning of the next
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Natural Language Understanding is primarily used in text based applications and dialogue based applications. TEXT BASED APPLICATIONS As the name implies text based applications deal with processing of written text such as books, newspapers, manuals, reports, e-mail messages, and so on. Natural language understanding techniques are widely used in finding the required documentation on certain topics from a
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