In this paper, four state-of-art probabilistic taggers i.e. TnT tagger, TreeTagger, RF tagger and SVM tool, are applied to the Urdu language. For the purpose of the experiment, a syntactic tagset is proposed. A training corpus of 100,000 tokens is used to train the models. Using the lexicon extracted from the training corpus, SVM tool shows the best accuracy of 94.15%. After providing a separat...

There are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amoun...

In this paper we describe our text preprocessing modules for English text-to-speech synthesis. These modules comprise rule-based text normalization subsuming sentence segmentation and normalization of non-standard words, statistical part-of-speech tagging, and statistical syllabification, grapheme-to-phoneme conversion, and word stress assignment relying in parts on rule-based morphological ana...

Markov models are one of the widely used techniques in machine learning to process natural language. Chains and Hidden Models stochastic employed for modeling systems that dynamic where future state relies on current state. The chain, which generates a sequence words create complete sentence, is frequently generating hidden model named-entity recognition tagging parts speech, tries predict tags...

There are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amoun...

This paper examines three ways to improve part-of-speech tagging accuracy: by increasing the number of training examples presented to the tree learner, by increasing the number of word-specific subtrees grown, and by increasing the number of ngrams (preceding parts of speech) per training example. Though experimental results indicate that additional training data generally leads to the greatest...

This paper investigates factors contributing to the performance of the POS Tagger for Tagalog language. Tagalog, a morphologically rich language, exhibits complex morphological structure, makes use of morphological information in determining parts of speech of the word, aspect and voice. As word feature information plays important role in efficient tagging, tag set definition capturing word inf...