AnnCorra

Bharati, Agehananda; Sangal, Rajeev; Chaitanya, Vineet; Kulkarni, Amba; Sharma, Dipti Misra; Ramakrishnamacharyulu, K. V.

doi:10.3115/1118759.1118761

Cited by 9 publications

(3 citation statements)

References 0 publications

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“…There have been no public projects, which are based on the extraction of parallel corpus. Some of the projects build parallel corpus from Wikipedia [6]. There are two approaches to align the sentences to build the corpora (Dutch English).…”

Section: Literature Surveymentioning

confidence: 99%

Building English-Punjabi Parallel corpus for Machine Translation

Jindal¹,

Goyal²,

Bhullar³

2017

IJCA

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ObjectiveParallel corpus is the key resource for English Punjabi machine translation. At wide level there is no availability of EnglishPunjabi corpora. There is a primary requirement of parallel corpus for the training of statistical machine translation. Methods/AnalysisIn this paper, authors focus on building English-Punjabi corpus at large scale. It posed difficulties and the intensive labor to develop the corpus. We are intricate on the collection as well as the flow of work for the construction of parallel corpus. Now after getting the raw text, we need to refine the corpus in such a way that every source language sentence should have corresponding target language sentence. FindingsThe paper attempts to explore existing tools as well as building new tools. One of the goals is alignment of bilingual corpus. The alignment algorithms are used to tune the sentences. The accuracy depends on the type of corpus. Novelty/ImprovementA cautious endeavor has been made to capture different types of texts.

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Section: Literature Surveymentioning

confidence: 99%

Building English-Punjabi Parallel corpus for Machine Translation

Jindal¹,

Goyal²,

Bhullar³

2017

IJCA

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“…A simple dependency parser for Hindi was developed by Bharati et al (2009). The parser used a grammar-oriented approach, which was designed on the basis of Paninian grammatical model (Bharati et al 1995; Begum et al 2008) discussed earlier. The differences between the two stage dependency parser for Hindi (Bharati et al 2008) and the Urdu parser presented earlier are also applied in the case of the simple dependency parser.…”

Section: Related Workmentioning

confidence: 99%

Morphologically rich Urdu grammar parsing using Earley algorithm

Abbas¹

2015

Nat. Lang. Eng.

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This work presents the development and evaluation of an extended Urdu parser. It further focuses on issues related to this parser and describes the changes made in the Earley algorithm to get accurate and relevant results from the Urdu parser. The parser makes use of a morphologically rich context free grammar extracted from a linguistically-rich Urdu treebank. This grammar with sufficient encoded information is comparable with the state-of-the-art parsing requirements for the morphologically rich Urdu language. The extended parsing model and the linguistically rich extracted-grammar both provide us better evaluation results in Urdu/Hindi parsing domain. The parser gives 87% of f-score, which outperforms the existing parsing work of Urdu/Hindi based on the tree-banking approach.

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“…Natural language processing (NLP) [47][48][49][50][51] utilises computer systems to analyse, understand and generate language such as that which human beings naturally use every day. It is an important field of artificial intelligence (AI) in computer science.…”

Section: Semantic Space Modelsmentioning

confidence: 99%

Semantic Space models for classification of consumer webpages on metadata attributes

Chen

Warren

Riddle

2010

Journal of Biomedical Informatics

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To deal with the quantity and quality issues with online healthcare resources, creating web portals centred on particular health topics and/or communities of users is a strategy to provide access to a reduced corpus of information resources that meet quality and relevance criteria. In this paper we use hyperspace analogue to language (HAL) to model the language use patterns of webpages as Semantic Spaces. We have applied machine learning methods, including support vector machine (SVM), decision forest, and a novel summed similarity measure (SSM) to automatically classify online webpages on their Semantic Space models. We find classification accuracy on metadata attributes to be over 93% for 'medical' versus 'supportive' perspective, over 92% for disease stage of 'early' versus 'advanced', and over 90% for author credentials of 'lay' versus 'clinician' based on webpages of the Breast Cancer Knowledge Online portal. These results indicate that language use patterns can be used to automate such classification with useful levels of accuracy.

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AnnCorra

Cited by 9 publications

References 0 publications

Building English-Punjabi Parallel corpus for Machine Translation

Building English-Punjabi Parallel corpus for Machine Translation

Morphologically rich Urdu grammar parsing using Earley algorithm

Semantic Space models for classification of consumer webpages on metadata attributes

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