Sinhala language, being the native language of the Sinhalese people , who make up the largest ethnic group of the island country of Sri Lanka, enjoys being reported as the mother tongue of Approximately 16 million people . To give a brief linguistic background for the purpose of aligning the Sinhala language with the baseline of English, primarily it should be noted that Sinhala language belongs same the Indo-European language tree . However, unlike English which is part of the Germanic branch, Sinhala belongs to the Indo-Aryan branch. Further, Sinhala, unlike English, which borrowed the Latin alphabet, has its own writing system, which is a descendant of the Indian Brahmi script [4, 5, 6, 7, 8, 9]. By extension, this makes Sinhala Script a member of the Aramaic family of scripts [10, 11]. It should be noted that the modern Sinhala language have loanwords from languages such as Tamil, English, Portuguese, and Dutch due to various historical reasons. Regardless of the rich historical array of literature spanning several millennia starting between to century BCE , modern natural language processing tools for the Sinhala language are scarce .
Natural Language Processing (NLP) is a broad area covering all computational processing and analysis of human languages. To achieve this end, NLP systems operate at different levels [14, 15]. A graphical representation of NLP layers and application domains are shown in Figure 1. On one hand, according to Liddy , these systems can be categorized into the following layers; phonological, morphological, lexical, syntactic, semantic, discourse, and pragmatic. The phonological layer deals with the interpretation of language sounds. As such, it consists of mainly speech-to-text and text-to-speech systems. In cases where one is working with written text of the language rather than speech, it is possible to replace this layer with tools which handle Optical Character Recognition (OCR) and language rendering standards (such as Unicode ). The morphological layer analyses words at their smallest units of meaning. As such, analysis on word lemmas and prefix-suffix-based inflection are handled in this layer. Lexical layer handles individual words. Therefore tasks such as Part of Speech (PoS) tagging happens here. The next layer, syntactic, takes place at the phrase and sentence level where grammatical structures are utilized to obtain meaning. Semantic
layer attempts to derive the meanings from the word level to the sentence level. Starting with Named Entity Recognition (NER) at the word level and working its way up by identifying the contexts they are set in until arriving at overall meaning. Thediscourse layer handles meaning in textual units larger than a sentence. In this, the function of a particular sentence maybe contextualized within the document it is set in. Finally, the pragmatic layer handles contexts read into contents without having to be explicitly mentioned [14, 15]. Some forms of anaphora (co-reference) resolution fall into this application.
On the other hand, Wimalasuriya and Dou  categorizes NLP tools and research by utility. They introduce three categories with increasing complexity; Information Retrieval (IR), Information Extraction (IE), and Natural Language Understanding (NLU). Information Retrieval covers applications which search and retrieve information which are relevant to a given query. For pure IR, tools and methods up-to and including the syntactic layer in the above analysis are used. Information Extraction, on the other hand, extracts structured information. The difference between IR and IE is the fact that IR does not change the structure of the documents in question. Be them structured, semi-structured, or unstructured, all IR does is fetching them as they are. In comparison, IE, takes semi-structured or unstructured text and puts them in a machine readable structure. For this, IE utilizes all the layers used by IR and the semantic layer. Natural Language Understanding is purely the idea of cognition. Most NLU tasks fall under AI-hard category and remain unsolved . However, with varying accuracy, some NLU tasks such as machine translation111This is, however, not without the criticism of being nothing more than a Chinese room  rather than true NLU. are being attempted. The pragmatic layer of the above analysis belongs to the NLU tasks while the discourse layer straddles information extraction and natural language understanding .
The objective of this paper is to serve as a comprehensive survey on the state of natural language processing resources for the Sinhala language. The initial structure and content of this survey are heavily influenced by the preliminary surveys carried out by de Silva  and Wijeratne et al. . However, our hope is to host this survey at arXiv as a perpetually evolving work which continuously gets updated as new research and tools for Sinhala language are created and made publicly available. Hence, it is our hope that this work will help future researchers who are engaged in Sinhala NLP research to conduct their literature surveys efficiently and comprehensively. For the success of this survey, we shall also consider the Sri Lankan NLP tools repository, lknlp222https://github.com/lknlp/lknlp.github.io.
The remainder of this survey is organized as follows; Section 2 discusses the various tools and research available for Sinhala NLP. In this section we would discuss both pure Sinhala NLP tool and research as well as hybrid Sinhala-English work. We will also discuss research and tools which contributes to Sinhala NLP either along with or by the help of Tamil, the other official language of Sri Lanka. Finally, Section 3, concludes the survey.
2 Sinhala resources
In this section we generally follow the structure shown in Figure 1 for sectioning. However, in addition to that, we also discuss topics such as available corpora, other data sets, dictionaries, and WordNets. We focus on NLP tools and research rather than the mechanics of language script handling [19, 20].
Tamil, the other official language of Sri Lanka is also a resource poor language. However, due to the existence of larger populations of Tamil speakers worldwide, including but not limited to economic powerhouses such as India, there are more research and tools available for Tamil NLP tasks . Therefore, it is rational to notice that Sinhala and Tamil NLP endeavours can help each other. Especially, given the above fact, that these are official languages of Sri Lanka, results in the generation of parallel data sets in the form of official government documents and local news items. A number of researchers make use of this opportunity. We shall be discussing those applications in this paper as well. Further, there have been some fringe implementations which bridge Sinhala with other languages such as Japanese [21, 22].
For any language, the key for NLP applications and implementations is the existence of adequate corpora. On this matter, a relatively substantial Sinhala text corpus333https://osf.io/a5quv/ was created by Upeksha et al. [23, 24] by web crawling. Later a smaller Sinhala newes corpus444https://osf.io/tdb84/ was created by de Silva . Both of the above corpora are publicly available. However, none of these come close to the massive capacity and range of the existing English corpora. A word corpus of approximately 35,000 entries was developed by Weerasinghe et al. . But it does not seem to be online anymore. A number of Sinhala-English parallel corpora were introduced by Guzmán et al. . This includes a 600k+ Sinhala-English subtitle pairs555http://bit.ly/2KsFQxm initially collected by , 45k+ Sinhala-English sentence pairs from GNOME666http://bit.ly/2Z8q0fo, KDE777http://bit.ly/2WLY6bI, and Ubuntu888http://bit.ly/2wLVZGt. Guzmán et al.  further provided two monolingual corpora for Sinhala. Those were a 155k+ sentences of filtered Sinhala Wikipedia999http://bit.ly/2EQZ7oM and 5178k+ sentences of Sinhala common crawl101010http://bit.ly/2ZaQFZo.
As for Sinhala-Tamil corpora, Mohamed et al.  claim to have built a word aligned Sinhala-Tamil parallel corpus. However, at the time of writing this paper, it was not publicly available. A very small Sinhala-Tamil aligned parallel corpus created by Farhath et al.  using order papers of government of Sri Lanka is available to download111111http://bit.ly/2HTMEme.
2.2 Data Sets
Specific data sets for Sinhala, as expected, is scarce. However, a Sinhala PoS tagged data set [30, 31, 32] is available to download from github121212http://bit.ly/2Krhrbv. Further, a Sinhala NER data set created by Manamini et al.  is also available to download from github131313http://bit.ly/2XrwCoK.
Facebook has released FastText [34, 35, 36] models for the Sinhala language trained using the Wikipedia corpus. They are available as both text models141414http://bit.ly/2JXAyL8 and binary files151515http://bit.ly/2JY5J9c. Using the above models by Facebook, a group at University of Moratuwa has created an extended FastText model trained on Wikipedia, News, and official government documents. The binary file161616http://bit.ly/2WowH0h of the trained model is available to be downloaded. Herath et al. 
has compiled a report on the Sinhala lexicon for the purpose of establishing a basis for NLP applications.
A necessary component for the purpose of bridging Sinhala and English resources are English-Sinhala dictionaries. The earliest and most extensive Sinhala-English dictionary available for consumption was by Malalasekera . However, this dictionary is locked behind copyright laws and is not available for public research and development. The dictionary by Kulatunga  is publicly available for usage through an online web interface but does not provide API access or means to directly access the data set. The largest publicly available English-Sinhala dictionary data set is from a discontinued FireFox plug-in EnSiTip  which bears a more than passing resemblance to the above dictionary by Kulatunga . Hettige and Karunananda  claim to to have created a lexicon to help in their attempt to create a system capable of English-to-Sinhala machine translation.
There exists the government sponsored trilingual dictionary  which matches Sinhala, English, and Tamil. However, other than a crude web interface on the ministry website, there is no efficient API or any other way for a researcher to access the data of this dictionary. Weerasinghe and Dias  have created a multilingual place name database for Sri Lanka which may function both as a dictionary and a resource for certain NER tasks.
WordNets  are extremely powerful and act as a versatile component of many NLP applications. They encompass a number of linguistic properties which exist between the words in the lexicon of the language including but not limited to: hyponymy, hypernymy, synonymy, and meronymy. Their uses range from simple gazetteer listing applications  to information extraction based on semantic similarity [45, 46] or semantic oppositeness . An attempt has been made to build a Sinhala Wordnet . For a time it was hosted on  but it too is now defunct and all the data and applications are lost. However, even at its peak, due to the lack of volunteers for the crowd soured methodology of populating the WordNet, it was at best an incomplete product. Another effort to build a Sinhala Wordnet was initiated by Welgama et al.  independently from above; but it too have stopped progression even before achieving the completion level of above.
2.5 Morphological Analyzers
As shown in Fig 1, morphological analysis is a ground level necessary component for natural language processing. Given that Sinhala is a highly highly inflected language [51, 52, 13], a proper morphological analysis process is vital. However, the only prominent work on this avenue of research which could be found was a study which was restricted to morphological analysis of Sinhala verbs . There was no indication on whether this work was continued to cover other types of words. Further, other than this singular publication, no data or tools were made publicly accessible. Completely independent of the above, Welgama et al. 
attempted to evaluate machine learning approaches for Sinhala morphological analysis. Yet another independent attempt to create a morphological parser for Sinhala verbs was carried out byFernando and Weerasinghe . As a step on their efforts to create a system with the ability to do English to Sinhala machine translation, Hettige and Karunananda  also claim to have created a morphological analyzer, again void of any public data or code.
2.6 Part of Speech Taggers
The next step after morphological analysis is Part of Speech (PoS) tagging. The PoS tags differ in number and functionality from language to language. Therefore, the first step in creating an effective PoS tagger is to identifying the PoS tag set for the language. This work has been accomplished by Fernando et al.  and Dilshani et al. . Expanding on that, Fernando et al.  has introduced an SVM Based PoS Tagger for Sinhala and finally Fernando and Ranathunga 
give an evaluation of different classifiers for the task of Sinhala PoS tagging. While here it is obvious that there has been some follow up work after the initial foundation, it seems all of that has been internal to one research group at one institution as neither the data nor the tools of any of these findings have been made available for the use of external researchers. Several attempts to create a stochastic part of speech tagger for Sinhala has been done with the attempts byHerath and Weerasinghe  and Jayasuriya and Weerasinghe  being most notable. A hybrid PoS tagger for Sinhala language was proposed by Gunasekara et al. . Within a single group yet another set of studies was carried out to create a Sinhala PoS tagger starting with the foundation of Jayaweera and Dias 
which then extended to a Hidden Markov Model (HMM) based approach and an analysis of unknown words . Further, this group presented a comparison of few Sinhala PoS taggers that are available to them .
The PoS tagged data then needs to be handed over to a parser. This is an area which is not completely solved even in English due to various inherent ambiguities in natural languages. However, in the case of English, there are systems which provide adequate results  even if not perfect yet. A prosodic phrasing model for sinhala language has been implemented by Bandara et al. . While they do report reasonable results, yet again, do not provide any means for the public to access the data or the tools that they have developed. Work by Liyanage et al.  is also concentrated on this layer given that they have worked on formalizing a computational grammar for Sinhala. Kanduboda and Prabath  have worked on Sinhala differential object markers. Another parser for the Sinhala language has been proposed by Hettige and Karunananda  with a model for grammar .
2.8 Named Entity Recognition Systems
As shown in Fig 1, once the text is properly parsed, it has to be processed using a Named-Entity-Recognition (NER) system. An NER system for Sinhla named Ananya has been developed by Manamini et al.  and is available to download at GitHub 171717http://bit.ly/2XrwCoK. Another independent attempt on Sinhala NER has been done by Dahanayaka and Weerasinghe ; but data and code of that is not accessible to the public.
2.9 Semantic Tools
Applications of the semantic layer is more advanced than the ones below it in Figure 1. But even with the obvious lack of resources and tools, a number of attempts have been made on semantic level applications for the Sinhala Language. A Sinhala semantic similarity measure has been developed for short sentences by Kadupitiya et al. . This work has been then extended by Kadupitiya et al.  for the application use case of short answer grading. Data and tools for these projects are not publicly available. Text classification is a popular application on the semantic layer of the NLP stack. Nanayakkara and Ranathunga  have implemented a system which uses corpus-based similarity measures for this propose. A smaller implementation of Sinhala news classification has been attempted by de Silva . As mentioned above, their news corpus is publicly available181818https://osf.io/tdb84/. But it is extremely small and thus may not provide much use for extensive research. A word2vec based tool191919http://bit.ly/2QKI9Np
for sentiment analysis of Sinhala news comments is available. There have been multiple attempts to do word sense disambiguation for Sinhala. For this,Arukgoda et al.  have proposed a system based on synonyms while Marasinghe et al.  have proposed a system based on probabilistic modeling.
2.10 Phonological Tools
On the case of phonological layer, a Sinhala text-to-speech system was developed by Weerasinghe et al. . However, it is not publicly accessible and there is no further research to be found of the work on a Sinhala speech-to-text system. A separate group has done work on Sinhala text-to-speech systems independent to above . On the converse, Nadungodage et al.  has done a series of work on Sinhala speech recognition with special notice given to Sinhala being a resource poor language. This project divides its focus on: continuity 78], and speaker adaptation . Based on the earlier work by Weerasinghe et al. , Wasala et al.  have developed methods for Sinhala grapheme-to-phoneme conversion along with a set of rules for schwa epenthesis. This work was then extended by Nadungodage et al. .
2.11 Optical Character Recognition Tools
While it is not necessarily a component of the NLP stack shown in Fig 1, which follows the definition by Liddy , it is possible to swap out the bottom most phonological layer of the stack in favour of an Optical Character Recognition (OCR) layer. The earliest attempt for Sinhala OCR system has been by Dias et al. . Then it has been extended to be online and made available to use via desktops  and hand-held devices  with the ability to recognize handwriting. A separate group had also attempted Sinhala OCR  mainly involving the nearest-neighbor method . A yet another attempt on this problem has been taken by Rajapakse et al.  before the above two groups. A linear symmetry based approch was proposed by Premaratne and Bigun 
. A Sinhala handwriting OCR system which utilizes zone-based feature extraction has been proposed byDharmapala et al. .
A series of work has been done by a group towards English to Sinhala translation as mentioned in some of the above subsections. This work includes; building a morphological analyzer , lexicon databases , a transliteration system , an evaluation model , a computational model of grammar , and a multi-agent solution . Another group independently attempted English-to-Sinhala machine translation  with a statistical approach . Hettige and Karunananda [96, 97] have attempted to establish a theoretical basics for English to Sinhala machine translation. A very simplistic web based translator was proposed by Hettige and Karunananda .
Most of the cross Sinhala and Tamil work has been done in the domain of machine translation. A neural machine translation for Sinhala and Tamil languages was initiated byTennage et al. . Then they further enhanced it with transliteration and byte pair encoding  and used synthetic training data to handle the rare word problem . This project produced Si-Ta  a machine translation system of Sinhala and Tamil official documents. In the statistical machine translation front, Farhath et al.  worked on integrating bilingual lists. The attempts by Weerasinghe  and Sripirakas et al.  were also focused on statistical machine translation while Jeyakaran  attempted a kernel regression method. A yet another attempt was made by Pushpananda et al.  which they later extended with some quality improvements . While not related to Tamil, there have been attempts to link Sinhala NLP with Japanese by Herath et al.  and Kanduboda .
At this point, a reader might think, there seems to be a significant number of implementations of NLP for Sinhala. Therefore, how can one justify listing Sinhala as a resource poor language? The important point which is missing in that assumption is that in the cases of almost all of the above listed implementations and findings, the only thing that is publicly available for a researcher is a set of research papers. The corpora, tools, algorithm, and anything else that were discovered through these research are either locked away as properties of individual research groups or worse lost to the time with crashed ancient servers, lost hard drives, and expired web hosts. This reason and probably academic/research rivalry have caused these separate research groups not to cite or build upon the works of each-other. In many cases where similar work is done, it is a re-hashing on the same ideas adopted from resource rich languages because of, either the unavailability of or the reluctance to, refer and build on work done by another group. This has resulted in multiple groups building multiple foundations behind closed walls but no one ending up with a completed end-to-end NLP work-flow. In conclusion, what can be said is even though there are islands of implementations done for Sinhala NLP, they are of very small scale and/or are usually not readily accessible for further use and research by other researchers. Thus, so far, sadly, Sinhala stays a resource poor language.
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