语言与文风2016theatlantic.com
道本语（Toki Pona）：一门只有100个词的人造语言
Toki Pona: A created language that has only 100 words
导读
这篇文章介绍 Toki Pona：一种只有约 100 个词的人造语言，其核心理念是“少即是多”，用极简词汇加组合来表达复杂意思。对写作者来说，它是一次关于“限制如何塑造表达”的思想实验：词汇越少，越考验你如何把意思拆开、重新组合，也越容易暴露歧义。
可以直接用的写作要点
- 01
把复杂意思拆成少数基本概念再组合表达，往往比堆砌大词更清楚。
- 02
少用一词多义、依赖语境的词（如 cool、blue），它们看似省事，实际会让读者猜测。
- 03
给文章里的关键概念下定义时，避免用被定义的词解释自己，否则容易循环定义。
- 04
给自己设词汇或篇幅限制，是逼出简洁表达的有效练习。
HN 评论精选
一个词承载多重含义会牺牲精确性，所谓的“简单”反而制造理解负担。
关键在简单。而在 Toki Pona 里，“简单”字面上就等于“好”，两个概念合并成一个词 pona。英语里也有类似情况：cool 指温度低，但用来形容人时又不是；blue 指一种颜色，但涉及情绪时又不是。这绝不简单，老实说我也不觉得好听。
yongjik查看英文原文
> The point is simplicity. And in Toki Pona, simple is literally good. Both concepts are combined in a single word: pona. That's how you end up with words like English "cool", which means low(-ish) temperature, except when it's about people, or "blue", a particular color, except when it's about feelings. It's anything but simple, and honestly it doesn't sound good to me, either.
词义可以拆成少量语义维度，核心符号加组合规则就能覆盖全部表达；辅助语言应优先考虑易学。
这让我想到自然语言处理工具 word2vec：它把词变成向量，每一维代表词义的某个方面（比如是否指地点、是男是女），这些向量不是人设计的，而是从数据里学出来的——出现在相似语境里的词，向量也相似。于是可以做出“国王－男人＋女人＝女王”这样的运算，甚至能帮机器翻译：把两种语言里几个对应的词固定成同一向量，其余词也会靠拢。说到底，具体的词和符号并不重要，重要的是准确表示词义所需的那约 100 个维度；只是人说出一个百维向量，远比说一个词低效。但借此也许能找出表达所有词所需的最小符号集和组合规则。很多人抱怨这不像自然语言，可辅助语言的重点本就是好学易用。
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.
用有限“语义基元”定义所有词汇，可以避免循环定义，并让隐藏的歧义显现出来。
类似的项目还有语言学家在 70 年代初提出的“自然语义元语言”，它由约 70 个“基元”组成，其他所有词都能分解成这些基元——就像写词典时必须避免用被定义的词来解释自己。与 Toki Pona 等语言不同，它主要用于思想实验，以及分析现有语言中的歧义和双重含义。
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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