[Article] Voynich2Vec: Using FastText Word Embeddings for Voynich Decipherment

[Torsten]

There seems not to be a text reference to Figure 8 'Document vectors'.

Indeed, they describe Figure 8 in the text but don't give the reference: "we see similar important words in each section (where 'importance' is as measured by tf-idf). While we would remiss to call this a topic in the semantic sense, we nonetheless observe a kind of morphosyntactic 'topic' (for example, in the bath section, qo- words have very high tf-idf scores)" (Merrill & Baum, p. 7).

The first 3 paragraphs of Section 4.2 'Topic Modeling' seems to be most relevant. Would that be correct?

Topic modeling (or context-dependent self-similarity) is indeed relevant for the Voynich manuscript (see Timm & Schinner 2020, p. 2f). For instance Currier described distinct languages A and B. Gabriel Landini described "unevenness of the local character density along the text" (Landini 2001, p. 292). "It is possible to distinguish Currier A and B based on frequency counts of tokens containing the sequence <ed>. ... if <chedy> is used more frequently, this also increases the frequency of similar words, like <shedy> or <qokeedy> .... At the same time, also words using the prefix <qok-> are becoming more and more frequent, whereas words typical for Currier A like <chol> and <chor> vanish gradually" (Timm & Schinner 2020, p. 6).

There are a number of related observations. One interesting outcome is that the MUSE "algorithm was largely unable to align our Voynich vectors with those of many other works in a number of languages. Oddly, we often found that MUSE aligned many Voynich words to the same word in another language ..." (Merrill & Baum, p. 7). Since words in the VMS group into similarity clusters the algorithm was trying to map word clusters to words in natural languages: "When we looked closely at many of the alignments, it seemed like similar Voynich words were being mapped to similar (or the same) target language word" (Merrill & Baum, p. 8).