NLP Terminology in 5 Minutes
Tags: nlp, datascience, bigdata
Published: 2017-12-25 06:21:35

---
title: NLP Terminology in 5 Minutes
published: true
description: A brief summary of nlp te4rms
tags: nlp, data science, big data
---

| Term                    | Meaning                                  |
| ----------------------- | ---------------------------------------- |
| **Weights and Vectors** |                                          |
| TF-IDF                  | Each word weight is higher the more a word appears in a doc and not in corpus (other docs), as the word is special to our doc, it's weight in our doc is higher!<br />TF-IDF is abbreviation for **T**erm **F**requency **I**nverse **D**ocument **F**requency |
| length(TF-IDF, doc)     | num of distinct words in doc, for each word number in vector. |
| Word Vectors            | Calculate word vector:<br/>for each word w1 => for each 5 window words, make vectors increasingly<br />closer, v[w1] closer v[w2]<br />king - queen ~ man - woman // wow it will find that for you!<br />You can even download ready made word vectors |
| Google Word Vectors     | You can download ready made google trained vector words |
| **Text Structure**      |                                          |
| Part-Of-Speech Tagging  | word roles: is it verb, noun, …? it's not always obvious |
| Head of sentence        | head(sentence) most important word, it's not nessesaraly the first<br />word, it's the root of the sentence the most important word<br />she hit the wall => hit .<br />You build a graph for a sentence and it becomes the root. |
| Named entities          | People, Companies, Locations, …, quick way to know what text is about. |
| **Sentiment Analysis**  |                                          |
| Sentiment Dictionary    | love +2.9, hated: -3.2, "I loved you but now I hate you" =>  2.9 - 3.2 |
| Sentiment Entities      | Is it about the movie or about the cinema place? |
| Sentiment Features      | Camera/**Resolution** , Camera/**Convinience** |
| **Text Classification** | Decisions, Decisions: What's the Topic, is he happy, native english speaker?<br />Mostly supervised training: We have labels, then map new text to labels |
| Supervised Learning     | We have 3 sets, Train Set, Dev Set, Test Set. |
| Train Set               |                                          |
| Dev(=Validation) Set    | Tuning Parameters (and also to prevent overfitting), tune model |
| Test Set                | Check your model                         |
| Text Features           | Convert documents to be classified into features, <br />bags of words word vectors, can use TF-IDF |
| LDA                     | Latent Dirichlecht Allocation: LDA(Documents) => Topics<br />Technology Topic: Scala, Programming, Machine Learning  <br />Sport Topic: Football, Basketball, Skateboards (3 most important words)<br />Pick number # of topics ahead of time like 5 topics<br />Doc = Distribution(topics) probability for each topic<br />Topic = Distribution(words) technology topic higher probably over cpu word<br />Unsupervised, what topics patterns are there.  Good for getting the sense what the doc is about. |
| **Machine Reading**     |                                          |
| Entity Extraction       | EntityRecognition(text) => (EntityName -> EntityType)<br />("paul newman is a great actor") => [(PaulNewman -> Person)] |
| Entity Linking          | EntityLinking(Entity) => FixedMeaning<br />EntityLinking("PaulNewman") => "http://wikipedia../paul_newman_the_actor"<br />(and not the other paul newman based on text) |
| dbpedia                 | DB for wikipedia, machines can read it its a db.  Query DBPedia with SparQL |
| FRED (lib) / Pikes      | FRED(natural-language) => formal-structure |
| Resources               | https://www.youtube.com/watch?v=FcOH_2UxwRg<br />https://tinyurl.com/word-vectors |