Building Indian language data to empower language diversity in technology

In today’s interconnected world, the diversity of languages reflects the richness of human expression. At Oxford Languages, we understand the profound significance of linguistic diversity. We are committed to ensuring that language barriers do not hinder technological innovation. With this vision, we focus on creating language data for a wide range of languages, including Indian languages and other less commonly represented ones.

Why Indian languages? With its diverse languages and dialects, it represents a rich linguistic tapestry unlike any other. Despite this diversity, many language datasets for Indian languages are underdeveloped and underutilized. To bridge this gap, we have curated datasets for Indian languages, encompassing widely spoken languages like Hindi, Tamil, Marathi, Malayalam, etc. as well as lesser-spoken languages like Assamese, Nepali, etc.

Through investment in Indian language datasets, we aim to empower millions of users to engage with technology in their native languages, improving access to information and services.

Building Accurate Indian Language Data

Creating language datasets require a lot of different skills. We have built a robust network of computational linguists, lexicographers, language experts, and native speakers. This ensures our datasets are not only comprehensive but also culturally and linguistically accurate allowing innovative technologies to offer the most authentic language experience.  

We build different types of language data; however, as we are mostly known for our dictionaries, here’s an example of how we curate Indian language datasets. Specifically for dictionaries, here are two methods we use, along with several others:

Acquiring and Adapting Popular Third-Party Dictionaries

We start by getting a dictionary and thoroughly evaluating it to find any gaps. With the aim to provide accurate definitions/translations, spellings, and grammatical information for a wide range of words and phrases, we finalize the optimization specifications and bring on highly experienced lexicographers, linguists/language experts for that project. Broadly speaking, our quality benchmark ensures that the dictionary focuses on providing accurate, comprehensive, and accessible language information effectively to meet the diverse needs of users in various contexts. This includes accurate and consistent standard spellings, coverage of common, modern, and culturally significant words to reflect the authentic use of the language while also ensuring that all sensitive content is appropriately labeled.

Curating Dictionaries From Scratch

This process takes a bit longer. To start, we must identify the right corpus for the dictionary. The corpus (lemmas in the dictionary) is curated by the lexicographers working on them. They manually select the right lemmas for a given language, considering their usage in contemporary media (both mainstream and social), literary and cultural references, among other factors. We include words in contemporary use, and if non-standard forms are frequent enough, we include them in the word list too. After this, drawing on our experts’ deep engagement with the language, we start adding all relevant details to the dictionary.

By taking these approaches, our Indian language datasets cover a wide spectrum of languages and domains, from general vocabulary to specialized terminology. Whether it is powering machine translation systems or enabling sentiment analysis in regional languages, our datasets serve as the backbone of countless language technologies, driving innovation and efficiency across industries.

Through our ongoing efforts and collaborative partnerships, we aim to continue pushing the boundaries of linguistic innovation, fostering a world where language is not a barrier but a bridge to greater understanding and connectivity. 

Laxmi Singh
Laxmi Singh is the Head of Optimization & Development at Oxford Languages. She leads a team committed to enhancing linguistic diversity and ensuring language barriers do not impede technological innovation.


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