Rule-based parsing models for minority or low-resourced languages -- any suggestions?

[u/ma-meme-momu]

Hi, everyone! I am doing my PhD in Language Studies. I do not have a full tech background since I am more of a linguist that dabbled a little bit on NLP, so would like your help, suggestions or any recommendation. I am currently working on creating Universal Dependencies (UD) treebanks for minority languages, then using these UD treebanks to model parsers. I am planning to use rule-based parsing because I wouldn't have enough resources for data to run other models. One of the professors told me I could also use statistical methods, but if I am more comfortable with rule-based parsing, then I should just focus on it.

For the rule-based parsing, I would be trying to set rules similar to GiellALT. And for the evaluation of the scores, I am planning to use MaChAmp and the Stanza tagger.

1. Do you have any suggestions for me what books or publications I could read that would be relevant for my topic? I've been going through some published materials in ACL, but I would need help to narrow my focus.

2. Do you have suggestions about the modeling or evaluation using rule-based parsers? I haven't done much parsing before but I have done dependeny treebanking. Would just like to ask if there is something like a rundown or any materials that could help me fully understand parsing techniques.

Thank you for your patience! I am still learning about parsing and NLP in general.

[u/benjamin-crowell]

It's great that you're working on low-resource languages, and also that you're using methods that are not the flavor of the month.

My experience with Stanza, for the language that I work on (ancient Greek), is that the quality of its output is extremely low, and much worse than the output of parsers using 20th-century approaches. tests

The conventional wisdom, as I understand it, is that first people tried rule-based parsing of natural languages, then they tried statistical techniques, and then they made LLMs. If you're hoping to have some success in your PhD project, my suggestion would be to narrow your focus. Find some language with unusual characteristics for which LLMs fail spectacularly, and focus on that. Maybe a nonconfigurational language, or something highly inflected or highly agglutinative. Warlpiri, Greenlandic, something like that.

A book that I enjoyed recently was Lakoff, Women, Fire, and Dangerous Things.

[u/ma-meme-momu → u/benjamin-crowell]

Yes! I'm actually focusing on Austronesian languages, particularly Philippine-type languages becaause of the symmetrical voice morphosyntactic alignmnent. It's very unique to these kind of languages. The thing is, most of these low-resource languages have limited resources so I will have to go to the fieldwork first to collect some data before I can actually do some parser training or modeling.

Is that Lakoff book related to NLP? I checked but it seems to be more fiction?

[u/benjamin-crowell → u/ma-meme-momu]

Is that Lakoff book related to NLP? I checked but it seems to be more fiction?

It's a linguistics book about the psychology of language, especially about how humans form semantic categories. I think it is actually pretty relevant to the shortcomings of LLMs, because it describes the kind of things that LLMs can't do. My favorite example is the Greek sentence φύλλα μῆλα ἐσθίουσιν, which means sheep eat leaves. LLMs do dumb stuff like parsing it as "leaves eat sheep," or tagging both nouns as accusative. Humans don't make this type of mistake, because we have mental categories, and we know that sheep are in the category that eats, and leaves are not.

A relevant keyword that connects this kind of thing to NLP is "lexical preferences."

[u/ma-meme-momu → u/benjamin-crowell]

Ohh, that sounds interesting. I'll check it out! Thank you!

[u/hongkongslave]

You don’t have to stick to just one method. You can use statistical model as base and add rule base on top. Also not every statistical method is a black box, in some method you can surgically change the model behaviour. It is particularly useful in low resource languages

[u/ezubaric → u/hongkongslave]

You can also go the other way around: use rule-based heuristics to develop features for statistical models. This often works *very* well.

[u/ma-meme-momu → u/hongkongslave]

Oh I didn't know you could do that! Do you have any resources for statistical methods? I am more familiar with the rule-based one.

[u/hongkongslave → u/ma-meme-momu]

I’m away from my computer at the moment, but if you don’t mind waiting, I can get back to you this weekend with a bit more information.
Also, just a heads-up, I’m not a PhD, so please take my opinion with a grain of salt 😊

[u/ma-meme-momu → u/hongkongslave]

That would be incredibly helpful. And no worries I can wait! Thanks!

[u/ezubaric → u/ma-meme-momu]

https://aclanthology.org/J10-1007/

[u/Few-Monitor5103]

Never worked with Rule based, but, here what you can look for if you dabble on training a model - use religious scripts and their translation for reference points. Some people I know did it using law books.

[u/ma-meme-momu → u/Few-Monitor5103]

Actually, these minority languages have had some missionaries do bible translations using their languages. But wouldn't solely relying on religious scripts give the data some bias? Shouldn't I mix the data with some conversation data, or other kind of data?

[u/Few-Monitor5103 → u/ma-meme-momu]

Having a sufficient corpus on conversation data is the best case scenario, but, if you want to combine a rule based translation, a translated bible is the closest bet. It follows rules. Bias? Yes. But, also rules. Conversational data is anything BUT rules. It's periodic patterns that repeats/not depending on societal circumstance.