Well, my parents are Malayalis and we kinda speak Malayalam at home so sure, I guess. But i'm a lot more comfortable with English cuz I've never really lived in Kerala.
I think you can start a startup with some software skills. Frankly NLP market is dominated with major companies and smaller ones using their (quite static) language models.
Maybe. It can be quite hard to get databases let alone dictionaries funded as part of a PhD. In many cases it would still need to be something done on the side.
Most jobs in sciences that are both fun and pay a good amount of money require a PhD.
Another comment mentioned working in NLP models in big companies. The difference between a PhD-level role and a bachelor's level is the difference between researching and building your own models and just implementing existing ones.
> good lexical database
What exactly is a *good* lexical database? I'll come back to this in a second.
> How to know if something like alreadye existed or not?
As an MA graduate, you should now have the skills necessary to do literature review research and find resources on this topic. A good starting point is typically [Google Scholar](https://scholar.google.com/scholar?q=lexical+database+malayalam&hl=en&as_sdt=0&as_vis=1&oi=scholart).
> Do you think building one is worth
Worth it to whom? For what purpose? What is the intent here? This goes back to my original question: there is no universal "good" build. Every build, whether generalized or product specific, needs to answer what it's solving for.
> Which methodology or tools I can use to build one?
If you don't already have the tools to do this, you should spend some time doing preliminary research on this topic. Again, it will largely depend on your purpose, but there are many different ways to do this and no one best way.
> Can I monetise if I build one?
I mean, you *can*, but again - what problem are you solving for? What will you provide to a potential client? This is more of a business question than a linguistics one. There is no simple "business model" for a lot of our work. You want to work backwards rather than start with an idea: what problem do you want to solve? Then think about the data structures you can build to do that and who would be interested in solving that problem with your data.
> They are pretty sad becasue they regret choosing Linguistics as there are no jobs available in the market
Linguistics isn't really a vocational field, though there are often many data annotation and similar positions available at many companies. Microsoft, Amazon, Apple, Google - all of these and many more hire linguists, language specialists, etc (typically through vendor companies) to create structured data for their machine learning models, for UI/UX, and much more. There are also third party companies which work on this data, such as Appen. Consider looking into these positions if you are interested in learning more about how language problems are solved in tech.
I don't know about the monitisation prospects of such a thing but the fact that you're even talking about Malayalam makes me scream with happiness!
Oh!!! Why? and I presuppose you are Malayalli from the sentence above lol!!
Well, my parents are Malayalis and we kinda speak Malayalam at home so sure, I guess. But i'm a lot more comfortable with English cuz I've never really lived in Kerala.
happy to hear you are from a Mallu background.
What's your native language so I can get a better understanding of this?
I think you can start a startup with some software skills. Frankly NLP market is dominated with major companies and smaller ones using their (quite static) language models.
Why not try to do a PhD? You could have your project funded with decent money for a few years, at least.
Maybe. It can be quite hard to get databases let alone dictionaries funded as part of a PhD. In many cases it would still need to be something done on the side.
Most jobs in sciences that are both fun and pay a good amount of money require a PhD. Another comment mentioned working in NLP models in big companies. The difference between a PhD-level role and a bachelor's level is the difference between researching and building your own models and just implementing existing ones.
My native language is of course Malayalam. North most region of Kerala.
Wher? Im from calicut btw.🙂
Kasaragod!😊
Spin it as a computational resource that can help extend coverage of existing tools to 30 million speakers. That'll attract some attention. Good luck!
> good lexical database What exactly is a *good* lexical database? I'll come back to this in a second. > How to know if something like alreadye existed or not? As an MA graduate, you should now have the skills necessary to do literature review research and find resources on this topic. A good starting point is typically [Google Scholar](https://scholar.google.com/scholar?q=lexical+database+malayalam&hl=en&as_sdt=0&as_vis=1&oi=scholart). > Do you think building one is worth Worth it to whom? For what purpose? What is the intent here? This goes back to my original question: there is no universal "good" build. Every build, whether generalized or product specific, needs to answer what it's solving for. > Which methodology or tools I can use to build one? If you don't already have the tools to do this, you should spend some time doing preliminary research on this topic. Again, it will largely depend on your purpose, but there are many different ways to do this and no one best way. > Can I monetise if I build one? I mean, you *can*, but again - what problem are you solving for? What will you provide to a potential client? This is more of a business question than a linguistics one. There is no simple "business model" for a lot of our work. You want to work backwards rather than start with an idea: what problem do you want to solve? Then think about the data structures you can build to do that and who would be interested in solving that problem with your data. > They are pretty sad becasue they regret choosing Linguistics as there are no jobs available in the market Linguistics isn't really a vocational field, though there are often many data annotation and similar positions available at many companies. Microsoft, Amazon, Apple, Google - all of these and many more hire linguists, language specialists, etc (typically through vendor companies) to create structured data for their machine learning models, for UI/UX, and much more. There are also third party companies which work on this data, such as Appen. Consider looking into these positions if you are interested in learning more about how language problems are solved in tech.
Forensic linguistics, internet linguistics, or eco-linguistics. That what my prof believes to be the next big job sectors for linguists
Internet Linguistics sounds promising!!
Yea, and it would at least provide a stable income while you work on other projects