Part of speech tagging for Amharic using conditional random fields

Adafre, Sisay Fissaha

doi:10.3115/1621787.1621797

Cited by 14 publications

(16 citation statements)

References 4 publications

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“…The Faculty of Informatics at Addis Ababa University (Ethiopia) has become very active in the field as well, working on, amongst others, Amharic morphological analysis, part-of-speech tagging, machine translation and document classification (Alemayehu and Willett 2002;Weldesellassie 2003;Amsalu and Gibbon 2005;Adafre 2005;Argaw and Asker 2007;Anberbir and Takara 2009;Tachbelie and Menzel 2010). An overview of Amharic language technology research efforts can be found at http://nlp.amharic.org.…”

Section: A Brief Overview Of African Language Technologymentioning

confidence: 98%

Introduction to the special issue on African Language Technology

Pauw

Schryver

Pretorius

et al. 2011

Lang Resources & Evaluation

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In today's digital multilingual world, language technology is crucial for providing access to information and opportunities for economic development. With approximately two thousand different languages, Africa is a multilingual continent par excellence, presenting acute challenges for those seeking to promote and use African languages in the areas of business development, education and relief aid. In recent times a number of researchers and institutions, both from Africa and elsewhere, have come forward to share the common goal of developing capabilities in language technology for African languages. In 2009 and 2010, the first two workshops on African Language Technology were organized (De Pauw et al. 2009, 2010a as a forum to bring together a wide range of researchers working in this domain.

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Section: A Brief Overview Of African Language Technologymentioning

confidence: 98%

Introduction to the special issue on African Language Technology

Pauw

Schryver

Pretorius

et al. 2011

Lang Resources & Evaluation

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“…Using small training set of size (1000 words) Adafre et al [27]develop CRF POS tagger for Amharic language .The set of features that are used for training are composed of lexical features, morphological features, dictionary features, the previous two POS tags, and character bi-grams. The accuracy of the tagger is 74% because of the small training data set.…”

Section: E Conditional Random Filedmentioning

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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“…Manual annotation: this corpus is annotated morphologically using AncoraPipe annotation tool. 1 Four annotators were involved in this task and annotation speed was between 80 and 120 tokens/hour. Our Inter Annotator Agreement is around 94.98%.…”

Section: Manual Annotation and Amts Tag Setmentioning

confidence: 99%

“…CRFs are applied to many NLP fields such as name entity recognition [25], shallow parsing [37], information extraction from tables [33]. CRFs were used for POS-tagging in many languages, such as Amharic [1] and Tamil [24].…”

Section: Our Machine Learnersmentioning

confidence: 99%

“…Sentence length +α Sentence length max sentence length (1 + α) Sentence length (1) where Conf M is the computed confidence measure, Words Conf is the total of the given sentence words confidences, Sentence length represents the tokens number of the sentence and max sentences length is the number of tokens of the longest unlabeled data sentences. By varying ␣, we obtained a slightly better accuracy of 89.89% and an error reduction of 2.53% after 11 iterations with a value of ␣ equal to 3.…”

Section: Words Confmentioning

confidence: 99%

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Using confidence and informativeness criteria to improve POS-tagging in amazigh

Outahajala

Benajiba²,

Rosso

et al. 2015

Journal of Intelligent &Amp; Fuzzy Systems

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Amazigh is used by tens of millions of people mainly for oral communication. However, and like all the newly investigated languages in natural language processing, it is resource-scarce. The main aim of this paper is to present our POS taggers results based on two state of the art sequence labeling techniques, namely Conditional Random Fields and Support Vector Machines, by making use of a small manually annotated corpus of only 20k tokens. Since creating labeled data is very timeconsuming task while obtaining unlabeled data is less so, we have decided to gather a set of unlabeled data of Amazigh language that we have preprocessed and tokenized. The paper is also meant to address using semi-supervised techniques to improve POS tagging accuracy. An adapted self training algorithm, combining confidence measure with a function of Out Of Vocabulary words to select data for self training, has been used. Using this language independent method, we have managed to obtain encouraging results.

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Part of speech tagging for Amharic using conditional random fields

Cited by 14 publications

References 4 publications

Introduction to the special issue on African Language Technology

Introduction to the special issue on African Language Technology

Classifiers combination to arabic morphosyntactic disambiguation

Using confidence and informativeness criteria to improve POS-tagging in amazigh

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