WEBVTT 00:01.080 --> 00:01.910 All right, everyone. 00:01.950 --> 00:04.860 So next one is freeze is melting. 00:05.280 --> 00:09.760 So in our last video, we have seen Rule-based making now freespace. 00:09.810 --> 00:10.200 Amazing. 00:10.920 --> 00:12.420 So let me look at this one. 00:12.510 --> 00:19.620 Now, Fresia means whenever you are trying to achieve or some work, which is a combination, let's 00:19.620 --> 00:27.620 say something like the phrase you are searching for, but space Obama or let's say pendulous place Marka, 00:28.100 --> 00:30.630 harder, Washington, D.C., so forth. 00:30.780 --> 00:32.820 Let me help to create one face. 00:32.910 --> 00:33.960 Metcher object. 00:34.440 --> 00:35.350 Let me execute it. 00:35.790 --> 00:40.680 And there is a part of again speci that matcher module only necmi defined. 00:40.860 --> 00:42.770 All this key phrases. 00:43.590 --> 00:46.710 Now you just did not supply all those phrases. 00:46.900 --> 00:51.330 Basically we know how to apply to the other NHP model. 00:51.990 --> 00:54.660 And for that, we'll use this list comprehensive. 00:55.260 --> 01:02.220 And for each PAX inside this phrase list, we are just applying to the NLB and created document Objects 01:02.340 --> 01:02.670 Holtorf. 01:03.180 --> 01:10.260 So this particular thing, phase baktun will be a list of all those document objects. 01:10.770 --> 01:11.810 Let me run it. 01:13.140 --> 01:19.890 Let's display this phrase pattern, so that will get it back that and God define if you find the type 01:19.890 --> 01:23.850 of the very first phrase pattern, it will be kind of duck. 01:24.630 --> 01:25.200 Next this. 01:25.320 --> 01:29.450 We need to have this phrase pattern over matched object. 01:30.060 --> 01:31.920 So you can see the matches we have created. 01:31.950 --> 01:36.210 But now this matches like a phrase matched object. 01:36.690 --> 01:37.850 So let me add it. 01:39.070 --> 01:40.840 And you can see I've kept here stop. 01:41.630 --> 01:44.030 So STK, we need to keep it. 01:45.530 --> 01:49.550 Next, this let's define our document and apply it to the MLP model. 01:49.910 --> 01:54.320 So we will create a document object and let's find matches. 01:55.160 --> 01:59.210 So for matches V, how to apply this document to the matched object. 01:59.690 --> 02:00.770 Let me run it. 02:01.270 --> 02:02.900 And it will find the matches for us. 02:02.930 --> 02:08.090 Now, as of now, it is just given a simple index number only. 02:08.180 --> 02:11.510 But let's see the detailed description of those. 02:11.630 --> 02:15.320 Barack Obama, Angela Merkel and Washington, D.C.. 02:15.860 --> 02:19.280 So you can see the very first thing is getting. 02:20.450 --> 02:23.420 Just the string representation of our match up. 02:23.570 --> 02:28.750 So we hope, given the string representation of this particular patterns will be terminology list. 02:28.970 --> 02:33.110 So everyone, it will be a terminology list so you can even define multiple also. 02:33.440 --> 02:35.440 So let's see if you find something else like. 02:35.900 --> 02:39.520 Or if you add something else, like another terminology. 02:39.920 --> 02:47.210 So you'll be able to find it in this particular phrases like match due to these kinds of phrases. 02:47.340 --> 02:53.790 We help define and just like earlier, we have seen in a rule-based matting it is trying to extract 02:53.840 --> 02:57.080 where in a particular place in our document object. 02:57.560 --> 02:58.370 We got this. 02:59.380 --> 03:00.100 Match for. 03:00.370 --> 03:02.220 So from two to four, we got this. 03:02.410 --> 03:05.300 Angela Merkel day in from 79. 03:05.890 --> 03:10.870 It will be Barack Obama and from 19 to 22, it's of Washington, D.C.. 03:11.230 --> 03:16.930 So that is how easily you can do the phrase mismatching inside this space. 03:18.090 --> 03:19.230 NetSol, about this video. 03:19.350 --> 03:23.790 See you in the next video, we'll see some more stuff related to natural language processing.