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Toki Pona：只有一百个词的语言（2015）
Toki Pona: A Language with a Hundred Words (2015)
2016 — 2021，它一次次回到首页
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导读
这篇《大西洋月刊》的文章介绍了 Toki Pona——一种只有约一百个词根的人造语言，由加拿大语言学家 Sonja Lang 创造。文章探讨了这种极简语言如何用少量词汇组合表达复杂概念（如冰岛语中“指南针”意为“方向指示器”），以及它为何吸引了一批爱好者。
为什么经久不衰：它触及语言与思维关系的根本问题：最少需要多少词汇就能表达一切？这个实验性语言多年来持续引发讨论，对语言学、极简设计和认知科学感兴趣的人都有启发。
HN 评论精选
亲历者证实 Toki Pona 上手极快，但表达效率低于词汇量更大的语言
高中时我创办了世界语社团，每周授课，一年后学员就能用世界语流利交流。有个英语很吃力的国际学生，几个月后用世界语比英语说得还流利！我高三时有个新生极力推荐 Toki Pona，我们花了一个月学它，大家掌握的速度快得惊人。当然 Toki Pona 更“啰嗦”，表达一个想法往往要用很多词。但学起来非常有趣，我至今还记得不少。
elldoubleyew查看英文原文
In high school I was the founder of our (small) Esperanto Club. I gave Esperanto lessons each week at our club meeting designed as a one year course, usually by the end of the year if someone attended each week and did moderate self study outside of class they could communicate in Esperanto really well. We even had one international student who really struggled with english that after a few months of attending our club could communicate with us in Esperanto more fluently than English! We had one a freshman join during my senior year who was a huge advocate of Toki Pona as a conlang. We decided to devote a month of the club to Toki Pona instead of Esperanto and it was mind boggling how quickly everyone was able to get a grasp of it. Granted Toki Pona is much more "wordy" than Esperanto, you often have to use many words to convey an idea that you could express quickly in a language with a larger vocabulary. Regardless it was an absolute blast to learn and I'm surprised by how much I remember. Once I got to college there seemed to be a severe lack of interest in Esperanto, or any conlang for that matter, amongst the student body. I could never really keep enough people interested the same way I could in high school so I gave up after my sophomore year. I really miss teaching people Esperanto. I believe the club at my high school still runs to this day, I'd love to be able to go back and visit one day.
词汇越少，每个词的含义越依赖语境，反而更难掌握
只有一百个词的问题在于，这一百个词是任何语言里最难的一百个词。对外语学习者来说，简单的词反而难，难的词反而容易。比如学英语时“banish”只有一个意思，记住就行；而“turn out”有六七种含义，全靠语境判断。极端例子是向母语没有冠词的人解释“the”。
yongjik查看英文原文
I've said it before, but the problem with having only a hundred words is that they are the hardest one hundred words in any languages. To a foreign learner, easy words are hard, and hard words are easy. To someone learning English as a foreign language, "banish" is easy to understand: it has just one meaning, so once you memorize it, you can recognize it whenever it's used. "Turn out", on the other hand, has half a dozen meanings and you have to rely on context. In the extreme case, imagine explaining the word "the" to someone whose native language doesn't have an article.
用 word2vec 类比：Toki Pona 的本质是用少量语义原语组合表达一切
这让我想起自然语言处理工具 word2vec：它把词表示成向量，维度代表语义特征，且是从数据中学出来的。可以做“国王-男人+女人=王后”这样的运算。也许借助这种思路，我们能找出表达所有词义所需的最小符号集和组合规则。当然，说出一百维向量远不如说一个词高效。辅助语言要优化的本来就不是自然度，而是易学性。
Houshalter查看英文原文
This reminds me a lot of a natural language processing tool called word2vec. It reduces words into vectors, where each dimension represents something about the meaning about the word. E.g. whether it's a place, or if it's male or female. And these vectors are not designed by humans, but learned from data. It's optimized so that words that occur in similar contexts have similar vectors. E.g. places tend to occur near words talking about places, like "there" or "go", and male names tend to occur near "he" and "his", etc. You can do really interesting things with these vectors. Like 'King'-'man'+'woman'='queen'. Recently a computer program beat the word analogy part of an IQ test using this. It's useful in computer translation. Take two languages and constrain a few identical words to have the same vector. Then all the other words also end up with similar vectors. Anyway, with something like this, the actual words and symbols don't matter, just the 100 or so dimensions needed to accurately represent the meaning of every word. Of course speaking a 100 dimensional vector is way less efficient than a single word or symbol. But perhaps using this, we could come up with an absolute minimal set of symbols necessary. And some rules for combining them to get all words. I know a lot of the comments here are complaining that this doesn't make a good natural language. But that's not the point of an auxiliary language. The main thing auxiliary languages should optimize for is how easy it is to learn. Simpler is better. Reducing the words and grammar is hugely important. Another advantage of such an approach, is that it might be more amenable to machine translation. Which is another advantage for an auxiliary language.
语言学早有类似尝试：用约 70 个语义原语分解所有词汇
上世纪 70 年代语言学家提出过一个类似项目：自然语义元语言（Natural Semantic Metalanguage），约 70 个“语义原语”，所有其他词都能分解为它们的组合——就像编写没有循环定义的词典时必须做的那样。不过它主要用于思想实验和分析现有语言中的歧义。
zachrose查看英文原文
Cool. There's a similar project put forth by linguists in the early 70s called Natural Semantic Metalanguage, with about 70 "primes" that all other words can be factored out as, like you would have to do if you wrote a dictionary without circular definitions. Unlike Toki Pona or the other languages mentioned in the article, Natural Semantic Metalanguage is mostly used for thought experiments and to analyze existing languages for ambiguities and double meanings.* https://en.m.wikipedia.org/wiki/Natural_semantic_metalanguage *"Double meanings" may not be the right word, but is there a word to describe what happens when a system is non-orthagonal and the same piece winds up with multiple responsibilities?
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