Bhāṣācitra: Visualising the dialect geography of S outh A sia (2021)

Abstract: We present Bhāṣācitra, a dialect mapping system for South Asia built on a database of linguistic studies of languages of the region annotated for topic and location data. We analyse language coverage and look towards applications to typology by visualising example datasets. The application is not only meant to be useful for feature mapping, but also serves as a new kind of interactive bibliography for linguists of South Asian languages.

## introduction south asia is extremely linguistically diverse. there is a common saying illustrating this diversity, present in several languages of the region; it is given in hindi below. kos kos par pānī badle, cār kos par bānī. 'the taste of water changes every mile, and the language every four.' one issue with this vast scale of diversity is the difficulty it poses for linguists in collecting and cataloguing linguistic data, which further impedes comprehensive typological analysis. india alone contains known living speakers of 461 languages @xcite . 2 it is also difficult to assess the availability of linguistic literature for all of these languages, leading to gaps in the typological databases we end up compiling; print linguistic bibliographies for the region become outdated as new work is published and do not encode useful metadata, such as the specific dialect studied in each work or the linguistic features studied. in this paper we present bhās . ācitra, a database of linguistic sources for south asian languages that we have compiled and annotated, as well as a dialect mapping and visualising system built from the location data extracted from those sources. currently it includes 1104 labelled sources covering 311 lects. the site is online at http:// aryamanarora.github.io/bhasacitra. computational approaches to dialect geography have worked on many parts of the issue, including the compilation of broad databases of linguistic features @xcite , dialect identification and clustering on modern social media corpora @xcite , and statistical modelling of dialect groups (e.g. @xcite . south asia is a linguistic area @xcite , a region of typological convergence due to historical contact between speakers of languages of different families. families represented in south asia are indo-european, dravidian, austroasiatic, sino-tibetan, and some unclassified isolates (nihali, kusunda, and burushaski). visualisation of data for linguistic typology has a long history, beginning with the first lexical isogloss maps created by aggregating data from dialect surveys and with more recent work specifically for visualising historical change, such as @xcite . as linguists adopt computational methods that deal with vast amounts of data, it becomes a challenge for humans to interpret datasets. modern approaches to visualisation like visual analytics (va) try to address this issue @xcite . the use of point-based mapping in linguistic data visualisation is well-known, in e.g. wals @xcite . this format has been used to map data in south asian languages @xcite as well as the languages of iran @xcite . we develop this paradigm further to map areal language extents based on the location data in published linguistic fieldwork. ## data model we built bhās . ācitra to be an easy-to-use system for researchers with no computational background. we implemented the application in javascript on a statically-hosted webpage. there are three data files in json format, for reference metadata (in bibt e x-compatible format with additional fields for location and topic information; see appendix a), language metadata (traditional genetic classification and coordinates for reference locations), and the typological database (containing per-language per-location data). the primary interface is an interactive map displaying geographical points corresponding to locations from which language data has been collected. the map is generated and manipulated using the d3.js library which has a complete pipeline for web cartography @xcite . dialect zones are partitioned using the voronoi algorithm; for a point p k in the set of points p, its voronoi region r k is defined as all points closer to p k than to any other point. in the primary interface (see figure 3 ), zones are colour-coded by consensus genetic classification of the languages covering the zone, with circles (with size proportional to the number of sources) centered at the weighted average of the coordinates of descriptions of the languages. in the case where multiple languages share a zone, the rgb components of the colouring are averaged. ## compiling the database there are some existing bibliographies of language references for south asia. in compiling data for bhās . ācitra, we prioritised the incorporation of sources that provided the greatest coverage of language information, such as grammars and grammatical sketches, analysed corpora, and sociolinguistic surveys. we began with data from glottolog for broad coverage @xcite ; south asiaspecific sources we drew from are @xcite ; baart and baart-bremer (2001); perera (2021). we then searched for literature not included in existing bibliographies. many new sources were obtained from shodhganga, foot_3 a platform for open-access digitised theses completed at indian universities. these theses were difficult to access before the past decade, so from this resource we were able to incorporate many new references. we annotated information on topic coverage for every source (see ## mapping datasets to illustrate the value of areal visualisation of language features, we mapped two datasets: the phoneme inventories of a large number of indian languages from @xcite , and the outcomes of selected sound changes from sanskrit to the modern indo-aryan languages based on the jambu database @xcite parsed from turner @xcite @xcite @xcite . note that we only visually analyse the map in these examples; these observations would need to be corroborated with statistic analysis and modelling to result in any verifiable claims. ## future work we intend to maximise coverage of south asian languages in bhās . ācitra. in the interest of achieving this goal we welcome contributions to our opensource database on github: https://github. com/aryamanarora/bhasacitra . ultimately, this sort of database would be useful for all languages of the world, but we lack the domain knowledge for non-south asian languages, so we welcome any collaborators who feel this system would be beneficial. as for directions for technical work, bhās . ācitra would benefit from a sql database for faster querying and precomputation of some data (e.g. language circle sizes and coordinates) to improve performance in the browser. in the interface, we will explore continuous alternatives to discretised voronoi polygons, which force rigid transitions between lects 6 and do not show where location coverage is sparse. this will also help us with the issue of large polygons at the edges of our research area. also, a basemap with administrative boundaries and other contextual geographical information would be useful. all of these will require substantial changes to the code beyond the capabilities of visualisation with pure d3.js. bhās . ācitra is one step of our larger goal of improving the study of south asian languages with computational methods. our future work on historical/comparative linguistics @xcite and corpus linguistics for under-studied languages of the region will benefit from bhās . ācitra's 6 we thank both reviewers for pointing out this limitation. visualisation capabilities. ## conclusion we developed and presented bhās . ācitra, a database of linguistic resources for south asia and a language visualisation system based on location data from those resources. we analysed the coverage of our database and used the areal mapping system to visualise phoneme inventories and indo-aryan sound change outcomes. we hope that researchers find the tool useful especially as we move forward with studying the typology of south asian languages.