fix some references to supp figs

Author: paxtonfitzpatrick

paper/main.pdf

@@ -950,11 +950,11 @@ \section*{Discussion}
 line). The BERT embeddings of the lectures and questions do not show this
 property (Supp.~Fig.~\ldaVsBERT). We also examined per-question ``content
 matches'' between individual questions and individual moments of each lecture
-(Figs.~\ref{fig:question-correlations},~\ldaVsBERT). The time series plot of
-individual questions' correlations are different from each other when computed
-using LDA (e.g., the traces can be clearly visually separated), whereas the
-correlations computed from BERT embeddings of different questions all look very
-similar. This tells us that LDA is capturing some differences in content
+(Fig.~\ref{fig:question-correlations}, Supp.~Fig.~\ldaVsBERT). The time series
+plot of individual questions' correlations are different from each other when
+computed using LDA (e.g., the traces can be clearly visually separated), whereas
+the correlations computed from BERT embeddings of different questions all look
+very similar. This tells us that LDA is capturing some differences in content
 between the questions, whereas BERT is not. The time series plots of individual
 questions' correlations have clear ``peaks'' when computed using LDA, but not
 when computed using BERT. This tells us that LDA is capturing a ``match''
@@ -1013,17 +1013,17 @@ \section*{Discussion}
 computing simple word overlap metrics. For example, the Jaccard similarity
 between text $A$ and $B$ is computed as the number of unique words in the
 intersection of words from $A$ and $B$ divided by the number of unique words in
-the union of words from $A$ and $B$. In a supplementary analysis (Supp.
-Fig.~\jaccard), we compared the LDA-based question-lecture matches we reported
-in Figure~\ref{fig:question-correlations} with the Jaccard similarities between
-each question and each sliding window of text from the corresponding lecture.
-As shown in Supplementary Figure~\jaccard, this simple word-matching approach
-does not appear to capture the same level of specificity as the LDA-based
-approach. Whereas the LDA-based approach often yields a clear peak in the
-time series of correlations between each question and the corresponding lecture,
-the Jaccard similarity-based approach does not. Furthermore, these LDA-based
-matches appear to capture conceptual overlaps between the questions and
-lectures (Supp.~Tab.~\matchTab), whereas simple word matching does not. For
+the union of words from $A$ and $B$. In a supplementary analysis
+(Supp.~Fig.~\jaccard), we compared the LDA-based question-lecture matches we
+reported in Figure~\ref{fig:question-correlations} with the Jaccard similarities
+between each question and each sliding window of text from the corresponding
+lecture. As shown in Supplementary Figure~\jaccard, this simple word-matching
+approach does not appear to capture the same level of specificity as the
+LDA-based approach. Whereas the LDA-based approach often yields a clear peak in
+the time series of correlations between each question and the corresponding
+lecture, the Jaccard similarity-based approach does not. Furthermore, these
+LDA-based matches appear to capture conceptual overlaps between the questions
+and lectures (Supp.~Tab.~\matchTab), whereas simple word matching does not. For
 example, one of the example questions examined in Supplementary
 Figure~\jaccard~asks ``Which of the following occurs as a cloud of atoms gets
 more dense?'' The LDA-based matches identify lecture timepoints where the