From 7d45af2d251e977a50744112c67743cb396456a8 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 2 Jun 2021 22:12:25 -0400
Subject: [PATCH] NLP 3 quiz

---
 NLP/2-Tasks/README.md                    |  5 +--
 quiz-app/src/assets/translations/en.json | 56 ++++++++++++++----------
 2 files changed, 34 insertions(+), 27 deletions(-)

diff --git a/NLP/2-Tasks/README.md b/NLP/2-Tasks/README.md
index 881acc280..a32583422 100644
--- a/NLP/2-Tasks/README.md
+++ b/NLP/2-Tasks/README.md
@@ -1,8 +1,7 @@
 # Common Natural Language Processing Tasks and Techniques
 
 For most *Natural Language Processing* tasks, the text to be processed must be broken down, examined, and the results stored or cross referenced with rules and data sets. This allows the programmer to derive the meaning or intent or only the frequency of terms and words in a text.
-
-## [Pre-lecture quiz](link-to-quiz-app)
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/31/)
 
 
 Let's discover common techniques used in processing text. Combined with machine learning, these techniques help you to analyse large amounts of text efficiently. Before applying ML to these tasks, however, let's understand the problems encountered by an NLP specialist.
@@ -180,7 +179,7 @@ One possible solution to the task is [here](solution/bot.py)
 
 Take a task in the prior knowledge check and try to implement it. Test the bot on a friend. Can it trick them? Can you make your bot more 'believable?'
 
-## [Post-lecture quiz](link-to-quiz-app)
+## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/32/)
 
 ## Review & Self Study
 
diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index b333dfdb4..53858662a 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -1663,48 +1663,52 @@
 				"title": "NLP Tasks: Pre-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "Tokenization",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "Splits text by means of punctuation",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
+								"answerText": "Splits text into separate tokens (words)",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "c",
+								"answerText": "Splits text into phrases",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "Embeddings",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "converts text data numerically so words can cluster",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "embeds words into phrases",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "embeds sentences into paragraphs",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "Parts-of-Speech Tagging",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "divides sentences by their parts of speech",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
+								"answerText": "takes tokenized words and tags them by their part of speech",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "c",
+								"answerText": "diagrams sentences",
 								"isCorrect": "false"
 							}
 						]
@@ -1716,48 +1720,52 @@
 				"title": "NLP Tasks: Post-Lecture Quiz",
 				"quiz": [
 					{
-						"questionText": "q1",
+						"questionText": "Build a dictionary of how often words reocccur using:",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "Word and Phrase Dictionary",
 								"isCorrect": "false"
 							},
 							{
-								"answerText": "b",
+								"answerText": "Word and Phrase Frequencies",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "c",
+								"answerText": "Word and Phrase Library",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q2",
+						"questionText": "N-grams refer to",
 						"answerOptions": [
 							{
-								"answerText": "a",
+								"answerText": "A text can be split into sequences of words of a set length",
 								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
+								"answerText": "A word can be split into sequences of characters of a set length",
+								"isCorrect": "false"
+							},
+							{
+								"answerText": "A text can be split into paragraphs of a set length",
 								"isCorrect": "false"
 							}
 						]
 					},
 					{
-						"questionText": "q3",
+						"questionText": "Sentiment analysis",
 						"answerOptions": [
 							{
-								"answerText": "a",
-								"isCorrect": "false"
+								"answerText": "analyzes a phrase for positivity or negativity",
+								"isCorrect": "true"
 							},
 							{
-								"answerText": "b",
-								"isCorrect": "true"
+								"answerText": "analyzes a phrase for sentimentality",
+								"isCorrect": "false"
 							},
 							{
-								"answerText": "c",
+								"answerText": "analyzes a phrase for sadness",
 								"isCorrect": "false"
 							}
 						]