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+---
+title: "Doing algebra with the meaning of words"
+layout: single
+excerpt: "how word embeddings can tell us about the relationship between words"
+tags: [til, statistics]
+---
+
+Learning lots of crazy stuff about embeddings from this great [guide](https://lena-voita.github.io/nlp_course/word_embeddings.html). The basic idea is to represent every word in a language by a vector which encodes its **semantic meaning**. You can then find similar words/topics by looking for vectors that are close to each other.
+
+It turns out that when you do these kind of word embeddings on large amounts of text there are many linear relationships between words. For example, the vector distance between **king** and **queen** is about the same as the distance between **man** and **woman**.
+
+This means you can perform a kind of addition and subtraction about the meaning of words!
+
+```
+bar - alcohol + coffee = cafe
+musician - music + science = researcher
+```
+
+This idea is similar to [how emojis are encoded](https://developers.mattermost.com/blog/all-about-emojis/), with the astronaut (👨‍🚀) emoji literally being represented as 👨/👩 + 🚀. This also used to encode all the different family combinations, e.g. 👨 + 👨 + 👧 = 👨‍👨‍👧
+
+# Learning translations
+
+The added bonus of this fact you can transfer what you learnt about one language to another, just need a small dictionary connecting the two languages and you can learn other translations for free!
+
+![](https://lena-voita.github.io/resources/lectures/word_emb/analysis/cross_lingual_matching-min.png)