Fletcher Hardison

Means, median distance between lemma clusters

Fletcher Hardison

I was reading a Koine Greek text last night and I realized that I was able to get a handle on certain new vocabulary because it was frequent within the section I was reading. I have no idea if it is frequent within the work as a whole, but the distance between encounters with the word short pretty short in the section I was reading. This got me wondering if this idea of distance would be helpful as a metric for vocabulary teaching/learning.

I wrote some code using the gnt_data from JTauber's vocabulary-tools. It's available in this gist.

The two tables below show the results of 25th-50th rows of the output. The first is sorted by mean and second by median. Looking at the output, it seems that the mean distance follows overall frequency (total). This which makes sense. It is, however, not the case for the median distance.

lemma mean median total -------- ------ -------- ------- διά 205.5 111 666 ἵνα 207.3 91 662 ἀλλά 211.9 100 638 ἀπό 213.5 131 644 ἔρχομαι 217.6 99 631 ποιέω 241.6 105 568 τίς 243 121 554 ἄνθρωπος 247 121 550 τις 256.1 126 530 Χριστός 258.6 55 528 οὖν 261.7 94 494 εἰ 271.1 131 502 ὡς 272.8 119.5 503 ὁράω 288 129 476 κατά 292.3 127 469 μετά 292.9 149 470 μαθητής 293.4 104 262 πατήρ 321.4 76 413 ἀκούω 321.6 159.5 427 πολύς 326.2 194 415 δίδωμι 326.7 151.5 415 Παῦλος 330.8 69 158 ἡμέρα 352.1 182 389 πνεῦμα 362.8 136 379 υἱός 365.7 163 375

Notice that the median does not follow frequency at all. The total number of occurrences and mean vary widely. The lower the median, the higher the probably that a word occurs multiple times close together. So we might expect a word like θηρίον to be rare within the corpus, but in certain sections it might be common.

lemma mean median total --------- ------ -------- ------- Χριστός 258.6 55 528 ὅς 97.4 55 1408 ἁμαρτία 787.7 55 172 θηρίον 2618.3 55 46 οὗτος 99.1 56 1385 ὅτι 105.5 56 1294 σάββατον 1526.1 56 68 χήρα 4335.8 56 26 περιτομή 1717 56 36 πᾶς 110.5 58 1244 Ἡρῴδης 1893.6 59 43 βαπτίζω 1334.6 59.5 77 Ἰησοῦς 152 62 906 Φίλιππος 2090.9 62 36 ὀμνύω 5178.8 63 26 μή 132.4 66 1036 γάρ 132 66 1039 ἐκ 150.8 69 913 Παῦλος 330.8 69 158 κύριος 192.7 70 713 σῶμα 944.1 70 142 Πέτρος 787.5 71 156 καυχάομαι 1079.7 73 36 ἐπί 155.3 74 885 οὐαί 2878.2 74 46

Just for kicks here is the median data, but this time showing rows 0 through 25.

μήτε 3885 4 34 ὁ 7 5 19769 οὔτε 1555.2 5 87 εἴτε 535.8 5 65 καί 15.3 10 8973 αὐτός 24.8 13 5546 γεννάω 1320 17 97 σύ 47.4 17 2894 ἐγώ 53.3 19 2572 ἀσπάζομαι 2151.9 22.5 59 λέγω 58.6 26 2345 ἐν 50.2 26 2733 δέ 49.4 27 2766 μέλος 3609.9 31 34 σπείρω 2319.3 34 52 εἰμί 55.9 35 2456 νόμος 622.8 36 193 ἑπτά 1492.5 40 88 οὐ 85.3 44 1605 θεός 105 45 1307 εἰς 78.1 45 1754 Αἴγυπτος 5474.6 45 25 Πιλᾶτος 1774.8 45 55 Καῖσαρ 3370.3 52 29 μνημεῖον 1768.3 53 40

In the end is this data particularly useful? Probably not. It could be helpful as a starting point to see if a word tends to cluster. If so, then it should be possible to use vocab-tools to find which pericopes, paragraphs, chapters etc. where the word clusters. This could be helpful in finding readings so that we can use a familiar corpus such as the GNT to teach words that might be rare within that corpus while being more common within the wider Greek corpus. Of course this could all be hogwash. Hopefully, I can write the code to find pericopes where the words cluster.