Application of multilayer perceptron network for tagging parts-of-speech

Ahmed, M.F.; Raju, S. Bapi; Chandrasekhar, P.V.S.; Prasad, M. H. M. Krishna

doi:10.1109/lec.2002.1182291

Cited by 5 publications

(3 citation statements)

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“…It has been known that the neural approaches only use a little amount of data to perform the training and learning stages. Moreover, the neural-based approaches not only consummate the associations (word-to-tag mappings) from a representative training data set but they can also be generalized to the unseen [1,17]. Overall, several advantages of the stochastic taggers can be identified over the rule-based taggers as they avoid the need for diligent manual rule building and probably obtain the useful information that may not be noticed by humans.…”

Section: The Differences Between Pos Tagger Modelsmentioning

confidence: 99%

Natural Language Processing based Soft Computing Techniques

Yousif¹

2013

IJCA

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This paper presents the implementation of soft computing (SC) techniques in the field of natural language processing. An attempt is made to design and implement an automatic tagger that extract a free text and then tag it. The part of speech taggers (POS) is the process of categorization words based on their meaning, functions and types (noun, verb, adjective, etc). Two stages tagging system based MPL, FRNN and SVM are implemented and designed. The system helps to classify words and assign the correct POS for each of them. The taggers are tested using two different languages (Arabic and Hindi). The Word disambiguation issue has been solved successfully for Arabic text. Experience has shown that the proposed taggers achieved a great accuracy (99%).

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Section: The Differences Between Pos Tagger Modelsmentioning

confidence: 99%

Natural Language Processing based Soft Computing Techniques

Yousif¹

2013

IJCA

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“…A multi-layer perception network tagger [42] is trained with error back -propagation learning algorithm. The tagger is trained with corpus of size (156622 words).…”

Section: J Artificial Neural Networkmentioning

confidence: 99%

Classifiers combination to arabic morphosyntactic disambiguation

Albared

Omar

Aziz

2009

2009 International Conference on Electrical Engineering and Informatics

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Parts of speech tagging forms the important preprocessing step in many of the natural language processing applications like text summarization, question answering and information retrieval system. MorphoSyntactic disambiguation (part of speech tagging) is the process of classifying every word in a given context to its appropriate part of speech. In this paper, we first review all the supervised machine learning approaches that have been used in the part of speech tagging. Then we review all the Arabic works to compare and to confirm our need to develop an accurate and efficient Arabic MorphoSyntactic Disambiguation system. Finally we propose a classifiers combination experimental framework for Arabic part of speech tagger in which three diverse probabilistic classifiers (Hidden Markov, Maximum Entropy and Transformation Based Learning) are combined using many different combination strategies to exploit their advantages Keywords-natural language processing, MorphoSyntactic disambiguation, machine learning.

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“…In the last few years, artificial neural network approach to POS tagging has been increasingly investigated due to its ability to learn the associations between words and tags and to generalize to unseen examples from a representative training data set. In (Schmid, 1994), a connectionist approach called Net-Tagger performed considerably well compared to statistical approaches; in (Benello et al, 1989) neural networks were used for syntactic disambiguation; in (Martín Valdivia, 2004), a Kohonen network was trained using the LVQ algorithm to increase accuracy in POS tagging; in (Marques and Pereira, 2001), feedforward neural networks were used to generate tags for unknown languages; recurrent neural networks were also used in (Pérez-Ortiz and Forcada, 2001) for this task; other examples are (Ahmed et al, 2002;Tortajada Velert et al, 2005).…”

Section: Introductionmentioning

confidence: 99%