--- a/doc/interpreter/external.txi	Wed Jun 04 21:26:07 2014 -0700
+++ b/doc/interpreter/external.txi	Fri Jun 06 09:43:54 2014 -0400
@@ -682,10 +682,10 @@
 ColumnVector cidx (nz);
 ColumnVector data (nz);
 
-ridx(0) = 0; cidx(0) = 0; data(0) = 1; 
-ridx(1) = 0; cidx(1) = 1; data(1) = 2; 
-ridx(2) = 1; cidx(2) = 3; data(2) = 3; 
-ridx(3) = 2; cidx(3) = 3; data(3) = 4;
+ridx(0) = 1; cidx(0) = 1; data(0) = 1;
+ridx(1) = 2; cidx(1) = 2; data(1) = 2;
+ridx(2) = 2; cidx(2) = 4; data(2) = 3;
+ridx(3) = 3; cidx(3) = 4; data(3) = 4;
 SparseMatrix sm (data, ridx, cidx, nr, nc);
 @end group
 @end example
@@ -807,7 +807,7 @@
       @}
     sm.cidx(j+1) = ii;
  @}
-sm.maybe_compress ();  // If don't know a-priori the final # of nz.
+sm.maybe_compress ();  // If don't know a priori the final # of nz.
 @end example
 
 @noindent
@@ -847,7 +847,7 @@
       @}
     sm.cidx(j+1) = ii;
  @}
-sm.maybe_mutate ();  // If don't know a-priori the final # of nz.
+sm.maybe_mutate ();  // If don't know a priori the final # of nz.
 @end example
 
 Note that both increasing and decreasing the number of non-zero elements in

--- a/doc/interpreter/interp.txi	Wed Jun 04 21:26:07 2014 -0700
+++ b/doc/interpreter/interp.txi	Fri Jun 06 09:43:54 2014 -0400
@@ -88,9 +88,9 @@
 
 @DOCSTRING(interpft)
 
-There are two significant limitations on Fourier interpolation.  Firstly,
+There are two significant limitations on Fourier interpolation.  First,
 the function signal is assumed to be periodic, and so non-periodic
-signals will be poorly represented at the edges.  Secondly, both the
+signals will be poorly represented at the edges.  Second, both the
 signal and its interpolation are required to be sampled at equispaced
 points.  An example of the use of @code{interpft} is
 

--- a/doc/interpreter/sparse.txi	Wed Jun 04 21:26:07 2014 -0700
+++ b/doc/interpreter/sparse.txi	Fri Jun 06 09:43:54 2014 -0400
@@ -47,7 +47,7 @@
 problems where only the non-zero elements of the matrix are
 stored.  Not only does this reduce the amount of memory to store the
 matrix, but it also means that operations on this type of matrix can
-take advantage of the a-priori knowledge of the positions of the
+take advantage of the a priori knowledge of the positions of the
 non-zero elements to accelerate their calculations.
 
 A matrix type that stores only the non-zero elements is generally called
@@ -73,7 +73,7 @@
 
 There are many different means of storing sparse matrix data.  What all
 of the methods have in common is that they attempt to reduce the complexity
-and storage given a-priori knowledge of the particular class of problems
+and storage given a priori knowledge of the particular class of problems
 that will be solved.  A good summary of the available techniques for storing
 sparse matrix is given by Saad @footnote{Y. Saad "SPARSKIT: A basic toolkit
 for sparse matrix computation", 1994,
@@ -573,7 +573,7 @@
 same manner as there full counterparts.  However, there are certain differences
 and especially differences with other products sparse implementations.
 
-Firstly, the @qcode{"./"} and @qcode{".^"} operators must be used with care. 
+First, the @qcode{"./"} and @qcode{".^"} operators must be used with care. 
 Consider what the examples
 
 @example

--- a/libinterp/corefcn/sparse.cc	Wed Jun 04 21:26:07 2014 -0700
+++ b/libinterp/corefcn/sparse.cc	Fri Jun 06 09:43:54 2014 -0400
@@ -81,16 +81,6 @@
 be added.  See @code{accumarray} for an example of how to produce different\n\
 behavior, such as taking the minimum instead.\n\
 \n\
-The following are all equivalent:\n\
-\n\
-@example\n\
-@group\n\
-s = sparse (i, j, s, m, n)\n\
-s = sparse (i, j, s, m, n, \"summation\")\n\
-s = sparse (i, j, s, m, n, \"sum\")\n\
-@end group\n\
-@end example\n\
-\n\
 Given the option @qcode{\"unique\"}, if more than two values are specified\n\
 for the same @var{i}, @var{j} indices, the last specified value will be\n\
 used.\n\