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@@ -14,7 +14,7 @@ Corpus-driven vocabulary review program for use with [Text-Fabric](https://annot
 
 Mahir is a **corpus-driven vocabulary review** program with an emphasis on **contextual learning of glosses**. The student of ancient corpora such as the Hebrew Bible or Greek New Testament is often faced with two sub-optimal choices when it comes to vocabulary acquisition. They may rely on traditional flash card systems (Anki, Quizlet, etc.), in which they must learn a word as a lexicalized form stripped of context. This technique of rote memorization is an unnatural way of learning language. It is difficult, slow, and ultimately not very helpful for idiomatic terms that strongly rely on context. The other option is to simply try to acquire vocabulary from rapid reading of text. While this is better from the standpoint of contextuality, it loses the benefit of systematically covering wide and diverse terms. It also makes the student a slave to the lexicon, which itself dulls the enjoyment of the reading process. This is especially problematic for highly poetic and abstract texts (e.g. Job in the Hebrew Bible). 
 
-**The corpus-driven approach blends the contextuality of reading the text with the systematicity of flash-card review**. This is made possible by using [Text-Fabric](github.com/annotation/text-fabric). For any given term in a vocabulary set, Mahir randomly selects a verse/line where the term appears. The "front side" of the "card" is the plain-text of the verse with the term at hand highlighted. The user can then score the term based on familiarity. In difficult cases, the user may request a different context, which is again selected at random. 
+**The corpus-driven approach blends the contextuality of reading the text with the systematicity of flash-card review**. This is made possible by using [Text-Fabric](https://github.com/annotation/text-fabric). For any given term in a vocabulary set, Mahir randomly selects a verse/line where the term appears. The "front side" of the "card" is the plain-text of the verse with the term at hand highlighted. The user can then score the term based on familiarity. In difficult cases, the user may request a different context, which is again selected at random. 
 
 ### Review Strategy