1 00:00:00,370 --> 00:00:01,530 ‫Now, Amazon Comprehend 2 00:00:01,530 --> 00:00:03,820 ‫is a very simple service from an exam perspective. 3 00:00:03,820 --> 00:00:05,610 ‫All you need to know is that Comprehend is 4 00:00:05,610 --> 00:00:06,540 ‫to comprehend stuff, 5 00:00:06,540 --> 00:00:09,928 ‫so it is for Natural Language Processing, or NLP. 6 00:00:09,928 --> 00:00:11,870 ‫So anytime you will see NLP on the exam, 7 00:00:11,870 --> 00:00:14,020 ‫you would think Amazon Comprehend. 8 00:00:14,020 --> 00:00:17,830 ‫Now what it is, it is a fully managed and serverless service 9 00:00:17,830 --> 00:00:20,040 ‫and it will use machine learning to find insights 10 00:00:20,040 --> 00:00:22,840 ‫and relationships in your text. 11 00:00:22,840 --> 00:00:24,758 ‫For example, it can understand what is the language 12 00:00:24,758 --> 00:00:27,490 ‫of the text, it can extract key phrases, 13 00:00:27,490 --> 00:00:30,460 ‫places, people, brands, or events from that text. 14 00:00:30,460 --> 00:00:33,340 ‫It can understand, do a sentiment analysis to understand 15 00:00:33,340 --> 00:00:37,570 ‫how positive or negative the text you're analyzing is. 16 00:00:37,570 --> 00:00:40,320 ‫It can also analyze a text using tokenization 17 00:00:40,320 --> 00:00:41,290 ‫and parts of speech. 18 00:00:41,290 --> 00:00:42,900 ‫It also has audio. 19 00:00:42,900 --> 00:00:45,190 ‫And finally, it can organize a collection 20 00:00:45,190 --> 00:00:48,720 ‫of text files by topic, and find topics itself. 21 00:00:48,720 --> 00:00:51,850 ‫So, Comprehend is really about getting a lot of data in 22 00:00:51,850 --> 00:00:53,010 ‫and then Comprehend will do the rest 23 00:00:53,010 --> 00:00:55,660 ‫to try to understand the meaning of that data. 24 00:00:55,660 --> 00:00:58,067 ‫So it's about taking text or unstructured data 25 00:00:58,067 --> 00:01:00,900 ‫and structuring it around these features. 26 00:01:00,900 --> 00:01:03,500 ‫So some sample use cases of Natural Language Processing, 27 00:01:03,500 --> 00:01:06,770 ‫or NLP, are for example, to analyze customer interaction. 28 00:01:06,770 --> 00:01:09,640 ‫So say you have a bunch of customers sending you emails 29 00:01:09,640 --> 00:01:11,800 ‫and you want to understand overall based 30 00:01:11,800 --> 00:01:14,190 ‫on your support service what leads to a positive 31 00:01:14,190 --> 00:01:16,260 ‫or negative experience from your customers. 32 00:01:16,260 --> 00:01:18,840 ‫So we use Comprehend to extract these features 33 00:01:18,840 --> 00:01:20,630 ‫and then you will have the business insight 34 00:01:20,630 --> 00:01:21,694 ‫and then you can improve your business, 35 00:01:21,694 --> 00:01:24,050 ‫thanks to that analysis. 36 00:01:24,050 --> 00:01:26,020 ‫You can also, for example, create 37 00:01:26,020 --> 00:01:29,080 ‫and group articles by topics that Comprehend will uncover. 38 00:01:29,080 --> 00:01:30,520 ‫So imagine you have a lot of articles 39 00:01:30,520 --> 00:01:32,450 ‫and you want to group them together instead 40 00:01:32,450 --> 00:01:34,740 ‫of going to them one by one, you would feed them 41 00:01:34,740 --> 00:01:36,920 ‫into Comprehend and then it would output you 42 00:01:36,920 --> 00:01:38,810 ‫the topics you need to group them by. 43 00:01:38,810 --> 00:01:39,643 ‫So that's it. 44 00:01:39,643 --> 00:01:41,500 ‫So again, a bit more information than what you need, 45 00:01:41,500 --> 00:01:44,060 ‫but Comprehend from an exam perspective is 46 00:01:44,060 --> 00:01:45,760 ‫for Natural Language Processing. 47 00:01:45,760 --> 00:01:46,593 ‫So hope you liked it, 48 00:01:46,593 --> 00:01:48,520 ‫and I will see you in the next lecture.