Ananya - a Named-Entity-Recognition (NER) system for Sinhala language

Manamini, S.A.P.M.; Ahamed, A.F.; Rajapakshe, R.A.E.C.; Reemal, G.H.A.; Jayasena, Sanath; Dias, Gihan; Ranathunga, Surangika

doi:10.1109/mercon.2016.7480111

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…However, a Sinhala PoS tagged data set [85][86][87] is available to download from github 17 . Further, a Sinhala NER data set created by Manamini et al [88] is also available to download from github 18 .…”

Section: Data Setsmentioning

confidence: 99%

“…The third attempt at Sinhala NER was by Manamini et al [88] who dubbed their system Ananya. They inherit the CRF model and ME baseline from the work of Dahanayaka and Weerasinghe [134].…”

Section: Named Entity Recognition Toolsmentioning

confidence: 99%

“…It should be noted that all three of these models only tag NEs of types: person names, location names and organization names. The Ananya system by Manamini et al [88] is available to download at GitHub 23 . The data and code for the approaches by Dahanayaka and Weerasinghe [134] and by Senevirathne et al [135] are not accessible to the public.…”

Section: Named Entity Recognition Toolsmentioning

confidence: 99%

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Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Silva¹

2019

Preprint

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Sinhala is the native language of the Sinhalese people who make up the largest ethnic group of Sri Lanka. The language belongs to the globe-spanning language tree, Indo-European. However, due to poverty in both linguistic and economic capital, Sinhala, in the perspective of Natural Language Processing tools and research, remains a resource-poor language which has neither the economic drive its cousin English has nor the sheer push of the law of numbers a language such as Chinese has. A number of research groups from Sri Lanka have noticed this dearth and the resultant dire need for proper tools and research for Sinhala natural language processing. However, due to various reasons, these attempts seem to lack coordination and awareness of each other. The objective of this paper is to fill that gap of a comprehensive literature survey of the publicly available Sinhala natural language tools and research so that the researchers working in this field can better utilize contributions of their peers. As such, we shall be uploading this paper to arXiv and perpetually update it periodically to reflect the advances made in the field.

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Section: Data Setsmentioning

confidence: 99%

“…The third attempt at Sinhala NER was by Manamini et al [88] who dubbed their system Ananya. They inherit the CRF model and ME baseline from the work of Dahanayaka and Weerasinghe [134].…”

Section: Named Entity Recognition Toolsmentioning

confidence: 99%

Section: Named Entity Recognition Toolsmentioning

confidence: 99%

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Survey on Publicly Available Sinhala Natural Language Processing Tools and Research

Silva¹

2019

Preprint

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“…However, not all methods described in section 2 can be used in the context of Sinhala and Tamil. For example, methods by Tóth et al (2008) and Mújdricza-Maydt et al (2013) cannot be used because NER systems and comprehensive POS taggers are not fully developed for Sinhala (Dahanayaka and Weerasinghe, 2014;Manamini et al, 2016) and Tamil (Pandian et al, 2008;Vijayakrishna and Devi, 2008). Also methods that align using the punctuations in the two languages similar to that of Chuang and Yeh (2005) cannot be used in this case because when extracting text from pdf, some punctuations are lost, and also the translators of the original text have not been consistent with the use of punctuations.…”

Section: Sentence Alignmentmentioning

confidence: 99%

Automatic creation of a word aligned Sinhala-Tamil parallel corpus

Mohamed

Ihalapathirana

Hameed

et al. 2017

2017 Moratuwa Engineering Research Conference (MERCon)

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A sentence aligned parallel corpus is an important prerequisite in statistical machine translation. However, manual creation of such a parallel corpus is time consuming, and requires experts fluent in both languages. Automatic creation of a sentence aligned parallel corpus using parallel text is the solution to this problem. In this paper, we present the first ever empirical evaluation carried out to identify the best method to automatically create a sentence aligned Sinhala-Tamil parallel corpus. Annual reports from Sri Lankan government institutions were used as the parallel text for aligning. Despite both Sinhala and Tamil being under-resourced languages, we were able to achieve an F-score value of 0.791 using a hybrid approach that makes use of a bilingual dictionary.

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