During last few years, I spent a lot of time learning foreign languages like Esperanto, Spanish and German. After a while, I came up with an idea that I can apply this knowledge in computer science.
When I decided this I was completely new to Computational Linguistics(CL) and Natural Language Processing(NLP). However after reading a number of articles I got some basic ideas.
What I am gonna do
To dive into CL/NLP I’ve decided implement Toki Pona -> English translator from scratch. It’s interesting to see which issues I will face and how I will solve them. It will make me go through number of stages of language processing:
- Lexical analysis
- Language detection (I want to distinguish Toki Pona from other languages)
- Morphological analysis (actually will be skipped because of simplicity of Toki Pona)
- Syntax analysis
- Word translation
- Syntax tree conversion
- Generation of final translation with respect to English grammar.
Anyway, this list is not strict, and probably it will be modified in the future.
What I am not gonna do
There are many tools and libraries that already exist in Ruby for NLP. I am not gonna use any of them here neither cover them in the articles. If you need something like that, please take a look at ruby-nlp. It’s a document that gathers a variety of NLP tools implemented in ruby.
What is Toki Pona?
Toki Pona is a constructed language created by Sonja Lang in 2001. What is so special about it? Its vocabulary is limited and contains only 125 words. The grammar is regular (anyway there will be some pitfalls). The language itself simple and can be learned in 1-2 nights, and I believe it allows to express 80-90% of daily human communication. Also, it has some philosophical background: speaking the language you realize what things really are.
Example: there is no word like “friend”, one would say “jan pona”, what literally means “good person/human”. In similar way “an ocean” is “telo suli” (big water), “juice” is “telo kili” (water of fruit or vegetable), etc.
So, even Toki Pona is not real natural language, it’s good to experiment with, and it gives me some hope that my goal can be achieved :)
And the end of this article you’ll find number of useful links if you want to get into the language.
First step: lexical analysis
The first step in processing natural or programming language is lexical analysis. It means splitting sequence of characters into some meaningful units: tokens. Sometimes the process is called tokenization and the tools that do it are tokenizers or lexical analyzers.
Let’s see an example. Given a sentence:
Translation: “Big man is good” (jan - human/man, suli - big, li - is/are, pona - good).
Note: in Toki Pona the main word goes first, so noun(jan) is on the first position, and on the second position is adjective(suli) that modifies the noun.
Expected list of tokens is
Let’s implement class
Tokipona::Tokenizer with a class method
.tokenize that returns an array of
tokens for a given text. We start with tests first.
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Usually lexical analysis for programming languages is based on finite-state automata. But in our simple case we can easily handle it with one regular expression:
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This implementation looks very naive, but specs pass, so we leave it as it is. Probably in the future we will modify.
It is the first article and the beginning of the journey. The next step will be an implementation of Toki Pona language detector. It’s not necessary to know Toki Pona to follow me, but in case you are interested, here below I provide some useful links, so you can learn yourself and start communicating.
I’ve created a github repository where you can access the code: greyblake/tokipona.
Thanks for reading. The subject is new for me, so your comments, suggestions and feedback can be very helpful.