Jaro-Winkler distance: Difference between revisions

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return 0.0;
return 0.0;
}
}
let delta = std::cmp::max(0, len2 / 2 - 1);
let delta = std::cmp::max(1, len2 / 2) - 1;
let mut flag = vec![false; len2];
let mut flag = vec![false; len2];
let mut ch1_match = vec![];
let mut ch1_match = vec![];

Revision as of 06:56, 1 August 2020

Jaro-Winkler distance is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.

The Jaro-Winkler distance is a metric for measuring the edit distance between words. It is similar to the more basic Levenstein distance but the Jaro distance also accounts for transpositions between letters in the words. With the Winkler modification to the Jaro metric, the Jaro-Winkler distance also adds an increase in similarity for words which start with the same letters (prefix).

The Jaro-Winkler distance is a modification of the Jaro similarity metric, which measures the similarity between two strings. The Jaro similarity is 1.0 when strings are identical and 0 when strings have no letters in common. Distance measures such as the Jaro distance or Jaro-Winkler distance, on the other hand, are 0 when strings are identical and 1 when they have no letters in common.

The Jaro similarity between two strings s1 and s2, simj, is defined as

simj = 0     if m is 0.
simj = ( (m / length of s1) + (m / length of s2) + (m - t) / m ) / 3     otherwise.

Where:

  •   is the number of matching characters (the same character in the same position);
  •   is half the number of transpositions (a shared character placed in different positions).


The Winkler modification to Jaro is to check for identical prefixes of the strings.

If we define the number of initial (prefix) characters in common as:

l = the length of a common prefix between strings, up to 4 characters

and, additionally, select a multiplier (Winkler suggested 0.1) for the relative importance of the prefix for the word similarity:

p   =   0.1

The Jaro-Winkler similarity can then be defined as

simw = simj + lp(1 - simj)

Where:

  • simj   is the Jaro similarity.
  • l   is the number of matching characters at the beginning of the strings, up to 4.
  • p   is a factor to modify the amount to which the prefix similarity affects the metric.

Winkler suggested this be 0.1.

The Jaro-Winkler distance between strings, which is 0.0 for identical strings, is then defined as

dw = 1 - simw

String metrics such as Jaro-Winkler distance are useful in applications such as spelling checkers, because letter transpositions are common typing errors and humans tend to misspell the middle portions of words more often than their beginnings. This may help a spelling checker program to generate better alternatives for misspelled word replacement.

The task

Using a dictionary of your choice and the following list of 9 commonly misspelled words:

"accomodate​", "definately​", "goverment​", "occured", "publically", "recieve​", "seperate", "untill​", "wich​"

  • Calculate the Jaro-Winkler distance between the misspelled word and words in the dictionary.
  • Use this distance to list close alternatives (at least two per word) to the misspelled words.
  • Show the calculated distances between the misspelled words and their potential replacements.
See also




Julia

<lang julia># download("http://users.cs.duke.edu/~ola/ap/linuxwords", "linuxwords.txt") const words = read("linuxwords.txt", String) |> split .|> strip

function jarowinklerdistance(s1, s2) 

   if length(s1) < length(s2)
       s1, s2 = s2, s1
   end
   len1, len2 = length(s1), length(s2)
   len2 == 0 && return 0.0
   delta = max(0, len2 ÷ 2 - 1)
   flag = zeros(Bool, len2)  # flags for possible transpositions, begin as false
   ch1_match = eltype(s1)[]
   for (i, ch1) in enumerate(s1)
       for (j, ch2) in enumerate(s2)
           if (j <= i + delta) && (j >= i - delta) && (ch1 == ch2) && !flag[j]
               flag[j] = true
               push!(ch1_match, ch1)
               break
           end
       end
   end
   matches = length(ch1_match)
   matches == 0 && return 1.0
   transpositions, i = 0, 0
   for (j, ch2) in enumerate(s2)
       if flag[j]
           i += 1
           transpositions += (ch2 != ch1_match[i])
       end
   end
   jaro = (matches / len1 + matches / len2 + (matches - transpositions/2) / matches) / 3.0
   commonprefix = count(i -> s1[i] == s2[i], 1:min(len2, 4))
   return 1 - (jaro + commonprefix * 0.1 * (1 - jaro))

end

function closewords(s, maxdistance, maxtoreturn) 

   jw = 0.0
   arr = [(w, jw) for w in words if (jw = jarowinklerdistance(s, w)) <= maxdistance]
   sort!(arr, lt=(x, y) -> x[2] < y[2])
   return length(arr) <= maxtoreturn ? arr : arr[1:maxtoreturn]

end

for s in ["accomodate", "definately", "goverment", "occured", "publically", 

   "recieve", "seperate", "untill", "wich"]
   println("\nClose dictionary words ( distance < 0.15 using Jaro-Winkler distance) to '$s' are:")
   println("    Word      |  Distance")
   for (w, jw) in closewords(s, 0.15, 5)
       println(rpad(w, 14), "| ", Float16(jw))
   end

end

</lang>

Output:
Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'accomodate' are:
    Word      |  Distance
accommodate   | 0.01819
accommodated  | 0.03333
accommodates  | 0.03333
accommodating | 0.08154
accommodation | 0.08154

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'definately' are:
    Word      |  Distance
definitely    | 0.04
defiantly     | 0.04224
define        | 0.08
definite      | 0.085
definable     | 0.0872

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'goverment' are:
    Word      |  Distance
government    | 0.05334
govern        | 0.06665
governments   | 0.0697
movement      | 0.081
governmental  | 0.0833

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'occured' are:
    Word      |  Distance
occurred      | 0.025
occur         | 0.05713
occupied      | 0.07855
occurs        | 0.09045
accursed      | 0.0917

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'publically' are:
    Word      |  Distance
publicly      | 0.04
public        | 0.08
publicity     | 0.10443
publication   | 0.1327
biblically    | 0.14

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'recieve' are:
    Word      |  Distance
receive       | 0.03333
received      | 0.0625
receiver      | 0.0625
receives      | 0.0625
relieve       | 0.06665

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'seperate' are:
    Word      |  Distance
desperate     | 0.07086
separate      | 0.0917
temperate     | 0.1042
separated     | 0.1144
separates     | 0.1144

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'untill' are:
    Word      |  Distance
until         | 0.03333
untie         | 0.1067
untimely      | 0.10834
Antilles      | 0.1263
untidy        | 0.1333

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to 'wich' are:
    Word      |  Distance
witch         | 0.05334
which         | 0.06
witches       | 0.11426
rich          | 0.11664
wick          | 0.11664


Python

<lang python>""" Test Jaro-Winkler distance metric. linuxwords.txt is from http://users.cs.duke.edu/~ola/ap/linuxwords """

WORDS = [s.strip() for s in open("linuxwords.txt").read().split()] MISSPELLINGS = [ 

   "accomodate​",
   "definately​",
   "goverment​",
   "occured",
   "publically",
   "recieve​",
   "seperate",
   "untill​",
   "wich​",

]

def jaro_winkler_distance(st1, st2): 

   """
   Compute Jaro-Winkler distance between two strings.
   """
   if len(st1) < len(st2):
       st1, st2 = st2, st1
   len1, len2 = len(st1), len(st2)
   if len2 == 0:
       return 0.0
   delta = max(0, len2 // 2 - 1)
   flag = [False for _ in range(len2)]  # flags for possible transpositions
   ch1_match = []
   for idx1, ch1 in enumerate(st1):
       for idx2, ch2 in enumerate(st2):
           if idx2 <= idx1 + delta and idx2 >= idx1 - delta and ch1 == ch2 and not flag[idx2]:
               flag[idx2] = True
               ch1_match.append(ch1)
               break

   matches = len(ch1_match)
   if matches == 0:
       return 1.0
   transpositions, idx1 = 0, 0
   for idx2, ch2 in enumerate(st2):
       if flag[idx2]:
           transpositions += (ch2 != ch1_match[idx1])
           idx1 += 1

   jaro = (matches / len1 + matches / len2 + (matches - transpositions/2) / matches) / 3.0
   commonprefix = 0
   for i in range(min(4, len2)):
       commonprefix += (st1[i] == st2[i])

   return 1.0 - (jaro + commonprefix * 0.1 * (1 - jaro))

def within_distance(maxdistance, stri, maxtoreturn): 

   """
   Find words in WORDS of closeness to stri within maxdistance, return up to maxreturn of them.
   """
   arr = [w for w in WORDS if jaro_winkler_distance(stri, w) <= maxdistance]
   arr.sort(key=lambda x: jaro_winkler_distance(stri, x))
   return arr if len(arr) <= maxtoreturn else arr[:maxtoreturn]

for STR in MISSPELLINGS: 

   print('\nClose dictionary words ( distance < 0.15 using Jaro-Winkler distance) to "',
         STR, '" are:\n        Word   |  Distance')
   for w in within_distance(0.15, STR, 5):
       print('{:>14} | {:6.4f}'.format(w, jaro_winkler_distance(STR, w)))

</lang>

Output:
Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " accomodate​ " are:
        Word   |  Distance
   accommodate | 0.0364
  accommodated | 0.0515
  accommodates | 0.0515
 accommodating | 0.0979
 accommodation | 0.0979

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " definately​ " are:
        Word   |  Distance
    definitely | 0.0564
     defiantly | 0.0586
        define | 0.0909
      definite | 0.0977
       defiant | 0.1013

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " goverment​ " are:
        Word   |  Distance
    government | 0.0733
        govern | 0.0800
   governments | 0.0897
      movement | 0.0992
  governmental | 0.1033

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " occured " are:
        Word   |  Distance
      occurred | 0.0250
         occur | 0.0571
      occupied | 0.0786
        occurs | 0.0905
      accursed | 0.0917

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " publically " are:
        Word   |  Distance
      publicly | 0.0400
        public | 0.0800
     publicity | 0.1044
   publication | 0.1327
    biblically | 0.1400

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " recieve​ " are:
        Word   |  Distance
       receive | 0.0625
      received | 0.0917
      receiver | 0.0917
      receives | 0.0917
       relieve | 0.0917

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " seperate " are:
        Word   |  Distance
     desperate | 0.0708
      separate | 0.0917
     temperate | 0.1042
     separated | 0.1144
     separates | 0.1144

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " untill​ " are:
        Word   |  Distance
         until | 0.0571
         untie | 0.1257
      untimely | 0.1321

Close dictionary words ( distance < 0.15 using Jaro-Winkler distance) to " wich​ " are:
        Word   |  Distance
         witch | 0.1067
         which | 0.1200

Raku

Works with: Rakudo version 2020.07

A minor modification of the Jaro distance task entry.

using the unixdict.txt file from www.puzzlers.org

<lang perl6>sub jaro-winkler ($s, $t) { 

   return 0 if $s eq $t;

   my $s_len = + my @s = $s.comb;
   my $t_len = + my @t = $t.comb;

   my $match_distance = ($s_len max $t_len) div 2 - 1;

   my @s_matches;
   my @t_matches;
   my $matches = 0;

   for ^@s -> $i {

       my $start = 0 max $i - $match_distance;
       my $end = $i + $match_distance min ($t_len - 1);

       for $start .. $end -> $j {
           @t_matches[$j] and next;
           @s[$i] eq @t[$j] or next;
           @s_matches[$i] = 1;
           @t_matches[$j] = 1;
           $matches++;
           last;
       }
   }

   return 1 if $matches == 0;

   my $k              = 0;
   my $transpositions = 0;

   for ^@s -> $i {
       @s_matches[$i] or next;
       until @t_matches[$k] { ++$k }
       @s[$i] eq @t[$k] or ++$transpositions;
       ++$k;
   }

   my $prefix = 0;

   ++$prefix if @s[$_] eq @t[$_] for ^(min 4, $s_len, $t_len);

   my $jaro = ($matches / $s_len + $matches / $t_len +
       (($matches - $transpositions / 2) / $matches)) / 3;

   1 - ($jaro + $prefix * .1 * ( 1 - $jaro) )

}


my @words = './unixdict.txt'.IO.slurp.words;

for <accomodate definately goverment occured publically recieve seperate untill wich> 

  -> $word {

  my %result = @words.race.map: { $_ => jaro-winkler($word, $_) };

  say "\nClosest 5 dictionary words with a Jaro-Winkler distance < .15 from $word:";

  printf "%15s : %0.4f\n", .key, .value for %result.grep({ .value < .15 }).sort({+.value, ~.key}).head(5);

}</lang>

Output:
Closest 5 dictionary words with a Jaro-Winkler distance < .15 from accomodate:
    accommodate : 0.0182
      accordant : 0.1044
       accolade : 0.1136
      acclimate : 0.1219
    accompanist : 0.1327

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from definately:
         define : 0.0800
       definite : 0.0850
        defiant : 0.0886
     definitive : 0.1200
      designate : 0.1219

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from goverment:
         govern : 0.0667
       governor : 0.1167
      governess : 0.1175
     governance : 0.1330
       coverlet : 0.1361

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from occured:
       occurred : 0.0250
          occur : 0.0571
      occurrent : 0.0952
        occlude : 0.1056
      concurred : 0.1217

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from publically:
         public : 0.0800
    publication : 0.1327
           pull : 0.1400
       pullback : 0.1492

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from recieve:
        receive : 0.0333
        relieve : 0.0667
          reeve : 0.0762
      receptive : 0.0852
      recessive : 0.0852

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from seperate:
      desperate : 0.0708
       separate : 0.0917
      temperate : 0.1042
       selenate : 0.1167
           sept : 0.1167

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from untill:
          until : 0.0333
           till : 0.1111
     huntsville : 0.1333
        instill : 0.1357
         unital : 0.1422

Closest 5 dictionary words with a Jaro-Winkler distance < .15 from wich:
          winch : 0.0533
          witch : 0.0533
          which : 0.0600
        wichita : 0.0857
         switch : 0.1111

Rust

Translation of: Python

<lang rust>use std::fs::File; use std::io::{self, BufRead};

fn load_dictionary(filename: &str) -> std::io::Result<Vec<String>> { 

   let file = File::open(filename)?;
   let mut dict = Vec::new();
   for line in io::BufReader::new(file).lines() {
       dict.push(line?);
   }
   Ok(dict)

}

fn jaro_winkler_distance(string1: &str, string2: &str) -> f64 { 

   let mut st1 = string1;
   let mut st2 = string2;
   if st1.len() < st2.len() {
       std::mem::swap(&mut st1, &mut st2);
   }
   let len1 = st1.len();
   let len2 = st2.len();
   if len2 == 0 {
       return 0.0;
   }
   let delta = std::cmp::max(1, len2 / 2) - 1;
   let mut flag = vec![false; len2];
   let mut ch1_match = vec![];
   for (idx1, ch1) in st1.chars().enumerate() {
       for (idx2, ch2) in st2.chars().enumerate() {
           if idx2 <= idx1 + delta && idx2 + delta >= idx1 && ch1 == ch2 && !flag[idx2] {
               flag[idx2] = true;
               ch1_match.push(ch1);
               break;
           }
       }
   }
   let matches = ch1_match.len();
   if matches == 0 {
       return 1.0;
   }
   let mut transpositions = 0;
   let mut idx1 = 0;
   for (idx2, ch2) in st2.chars().enumerate() {
       if flag[idx2] {
           transpositions += (ch2 != ch1_match[idx1]) as i32;
           idx1 += 1;
       }
   }
   let m = matches as f64;
   let jaro =
       (m / (len1 as f64) + m / (len2 as f64) + (m - (transpositions as f64) / 2.0) / m) / 3.0;
   let mut commonprefix = 0;
   for (c1, c2) in st1.chars().zip(st2.chars()).take(std::cmp::min(4, len2)) {
       commonprefix += (c1 == c2) as i32;
   }
   1.0 - (jaro + commonprefix as f64 * 0.1 * (1.0 - jaro))

}

fn within_distance<'a>( 

   dict: &'a Vec<String>,
   max_distance: f64,
   stri: &str,
   max_to_return: usize,

) -> Vec<(&'a String, f64)> { 

   let mut arr: Vec<(&String, f64)> = dict
       .iter()
       .map(|w| (w, jaro_winkler_distance(stri, w)))
       .filter(|x| x.1 <= max_distance)
       .collect();
   // The trait std::cmp::Ord is not implemented for f64, otherwise
   // we could just do this:
   // arr.sort_by_key(|x| x.1);
   let compare_distance = |d1, d2| {
       use std::cmp::Ordering;
       if d1 < d2 {
           Ordering::Less
       } else if d1 > d2 {
           Ordering::Greater
       } else {
           Ordering::Equal
       }
   };
   arr.sort_by(|x, y| compare_distance(x.1, y.1));
   arr[0..std::cmp::min(max_to_return, arr.len())].to_vec()

}

fn main() { 

   match load_dictionary("linuxwords.txt") {
       Ok(dict) => {
           for word in &[
               "accomodate",
               "definately",
               "goverment",
               "occured",
               "publically",
               "recieve",
               "seperate",
               "untill",
               "wich",
           ] {
               println!("Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to '{}' are:", word);
               println!("        Word   |  Distance");
               for (w, dist) in within_distance(&dict, 0.15, word, 5) {
                   println!("{:>14} | {:6.4}", w, dist)
               }
               println!();
           }
       }
       Err(error) => eprintln!("{}", error),
   }

}</lang>

Output:

The output is slightly different from that of the Python example because I've removed a trailing zero-width space character from some of the test strings. 

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'accomodate' are:
        Word   |  Distance
   accommodate | 0.0182
  accommodated | 0.0333
  accommodates | 0.0333
 accommodating | 0.0815
 accommodation | 0.0815

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'definately' are:
        Word   |  Distance
    definitely | 0.0400
     defiantly | 0.0422
        define | 0.0800
      definite | 0.0850
     definable | 0.0872

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'goverment' are:
        Word   |  Distance
    government | 0.0533
        govern | 0.0667
   governments | 0.0697
      movement | 0.0810
  governmental | 0.0833

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'occured' are:
        Word   |  Distance
      occurred | 0.0250
         occur | 0.0571
      occupied | 0.0786
        occurs | 0.0905
      accursed | 0.0917

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'publically' are:
        Word   |  Distance
      publicly | 0.0400
        public | 0.0800
     publicity | 0.1044
   publication | 0.1327
    biblically | 0.1400

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'recieve' are:
        Word   |  Distance
       receive | 0.0333
      received | 0.0625
      receiver | 0.0625
      receives | 0.0625
       relieve | 0.0667

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'seperate' are:
        Word   |  Distance
     desperate | 0.0708
      separate | 0.0917
     temperate | 0.1042
     separated | 0.1144
     separates | 0.1144

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'untill' are:
        Word   |  Distance
         until | 0.0333
         untie | 0.1067
      untimely | 0.1083
      Antilles | 0.1264
        untidy | 0.1333

Close dictionary words (distance < 0.15 using Jaro-Winkler distance) to 'wich' are:
        Word   |  Distance
         witch | 0.0533
         which | 0.0600
       witches | 0.1143
          rich | 0.1167
          wick | 0.1167

Wren

Library: Wren-fmt
Library: Wren-sort

This uses unixdict and borrows code from the Jaro_distance#Wren task. <lang ecmascript>import "io" for File import "/fmt" for Fmt import "/sort" for Sort

var jaroSim = Fn.new { |s1, s2| 

   var le1 = s1.count
   var le2 = s2.count
   if (le1 == 0 && le2 == 0) return 1
   if (le1 == 0 || le2 == 0) return 0
   var dist = (le2 > le1) ? le2 : le1
   dist = (dist/2).floor - 1
   var matches1 = List.filled(le1, false)
   var matches2 = List.filled(le2, false)
   var matches = 0
   var transpos = 0
   for (i in 0...s1.count) {
       var start = i - dist
       if (start < 0) start = 0
       var end = i + dist + 1
       if (end > le2) end = le2
       var k = start
       while (k < end) {
           if (!(matches2[k] || s1[i] != s2[k])) {
               matches1[i] = true
               matches2[k] = true
               matches = matches + 1
               break
           }
           k = k + 1
       }
   }
   if (matches == 0) return 0
   var k = 0
   for (i in 0...s1.count) {
       if (matches1[i]) {
           while(!matches2[k]) k = k + 1
           if (s1[i] != s2[k]) transpos = transpos + 1
           k = k + 1
       }
   }
   transpos = (transpos/2).floor
   return (matches/le1 + matches/le2 + (matches - transpos)/matches) / 3

}

var jaroWinklerDist = Fn.new { |s, t| 

   var ls = s.count
   var lt = t.count
   var lmax = (ls < lt) ? ls : lt
   if (lmax > 4) lmax = 4
   var l = 0
   for (i in 0...lmax) {
       if (s[i] == t[i]) l = l + 1
   }
   var js = jaroSim.call(s, t)
   var p = 0.1
   var ws = js + l*p*(1 - js)
   return 1 - ws

}

var misspelt = ["accomodate", "definately", "goverment", "occured", "publically", "recieve", "seperate", "untill", "wich"] var words = File.read("unixdict.txt").split("\n").map { |w| w.trim() }.where { |w| w != "" } for (ms in misspelt) { 

   var closest = []
   for (word in words) {
      var jwd = jaroWinklerDist.call(ms, word)
      if (jwd < 0.15) closest.add([word, jwd])
   }
   System.print("Misspelt word: %(ms):")
   var cmp = Fn.new { |n1, n2| (n1[1]-n2[1]).sign }
   Sort.insertion(closest, cmp)
   for (c in closest.take(6)) Fmt.print("$0.4f $s", c[1], c[0])
   System.print()

}</lang>

Output:
Misspelt word: accomodate:
0.0182 accommodate
0.1044 accordant
0.1136 accolade
0.1219 acclimate
0.1327 accompanist
0.1333 accord

Misspelt word: definately:
0.0800 define
0.0850 definite
0.0886 defiant
0.1200 definitive
0.1219 designate
0.1267 deflate

Misspelt word: goverment:
0.0667 govern
0.1167 governor
0.1175 governess
0.1330 governance
0.1361 coverlet
0.1367 sovereignty

Misspelt word: occured:
0.0250 occurred
0.0571 occur
0.0952 occurrent
0.1056 occlude
0.1217 concurred
0.1429 cure

Misspelt word: publically:
0.0800 public
0.1325 pullback
0.1327 publication
0.1400 pull

Misspelt word: recieve:
0.0333 receive
0.0667 relieve
0.0762 reeve
0.0852 receptive
0.0852 recessive
0.0905 recife

Misspelt word: seperate:
0.0708 desperate
0.0917 separate
0.1042 temperate
0.1048 repartee
0.1167 selenate
0.1167 sept

Misspelt word: untill:
0.0333 until
0.1111 till
0.1333 huntsville
0.1357 instill
0.1422 unital

Misspelt word: wich:
0.0533 winch
0.0533 witch
0.0600 which
0.0857 wichita
0.1111 switch
0.1111 twitch
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