《Elasticsearch 权威指南》之基础入门 Note（基于 7.x）

目录

## 为了搜索

Elasticsearch 建立在 Lucene 上，它不仅仅是一个全文搜索引擎：

  • 一个分布式的实时文档存储，每个字段 可以被索引与搜索
  • 一个分布式实时分析搜索引擎
  • 能胜任上百个服务节点的扩展，并支持 PB 级别的结构化或者非结构化数据

Install Elasticsearch with Docker | Elasticsearch Reference [7.2] | Elastic

  • docker run -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:7.2.0

  • curl http://127.0.0.1:9200/_cat/health

  • curl 'http://localhost:9200/?pretty'

Running Kibana on Docker | Kibana User Guide [7.2] | Elastic

  • docker run --link YOUR_ELASTICSEARCH_CONTAINER_NAME_OR_ID:elasticsearch -p 5601:5601 {docker-repo}:{version}

  • http://localhost:5601

存储数据到 Elasticsearch 的行为叫做 索引，但在索引一个文档之前，需要确定将文档存储在哪里。

一个 Elasticsearch 集群可以 包含多个 索引 ，相应的每个索引可以包含多个 类型 。 这些不同的类型存储着多个 文档 ，每个文档又有 多个 属性 。

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Syntactic Parsing Note (SLP Ch13)

This chapter focuses on the structures assigned by context-free grammars. Context-free grammars don’t specify how the parse tree for a given sentence should be computed. We therefore need to specify algorithms that employ these grammars to efficiently produce correct trees. They are useful in applications such as grammar checking, semantic analysis, question answering and information extraction.

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信息熵与选择：由三门问题想到的

今天看公众号文章正好看到一篇《三门问题》，虽然看似简单，但细想感觉很有意思，特意把自己的思考记录一下。三门问题出自上世纪 70 年代美国的一个综艺节目，基本描述是这样的：你是游戏的参与者，面前有三扇门，其中一扇门后面是一辆跑车，其他两扇后面什么都没有。在你选择一扇门后先不打开，主持人打开了另一扇门（后面必然是空的），此时你有两个选择，坚持选择刚才选的那扇，或者换另外一扇还没打开的。怎么选择才能让你得到跑车的概率最大？

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Sequence Processing with Recurrent Networks Note (SLP Ch09)

Problematic of the sliding window in general NN:

  • Like Markov it limits the context from which information can be extracted (limits to window area)
  • Window makes it difficult to learn systematic patterns arising from phenomena like constituency

RNN is a class of networks designed to address these problems by processing sequences explicitly as sequences, allowing us to handle variable length inputs without the use of arbitrary fixed-sized windows.

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Neural Networks and Neural Language Models Note (SLP Ch07)

Units

z=w⋅x+bz = w · x + b

y=σ(z)=11+e−zy = \sigma(z) = \frac{1}{1+e^{-z}}

In practice, the sigmoid is not commonly used as an activation function. A better one is tanh function ranges from -1 to 1: $$y = \frac{e^z - e^{-z}}{e^z + e^{-z}}$$

The most commonly used is the rectified linear unit, also called ReLU: y = max(x, 0)​

In the sigmoid or tanh functions, very high values of z result in values of y that are saturated, extremely close to 1, which causes problems for learning.

  • Rectifiers don’t have this problem, since the output of values close to 1 also approaches 1 in a nice gentle linear way.
  • By contrast, the tanh function has the nice properties of being smoothly differentiable and mapping outlier values toward the mean.

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Vector Semantics Note (SLP Ch06)

Words that occur in similar contexts tend to have similar meanings. This link between similarity in how words are distributed and similarity in what they mean is called the distributional hypothesis.

  • words which are synonyms tended to occur in the same environment
  • with the amount of meaning difference between two words “corresponding roughly to the amount of difference in their environments”

vector semantics instantiates this linguistic hypothesis by learning representations of the meaning of words directly from their distributions in texts.

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