## Word-level edit distance of a sentence

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Is there an algorithm that lets you find the word-level edit distance between 2 sentences? For eg., "A Big Fat Dog" and "The Big House with the Fat Dog" have 1 substitute, 3 insertions

You can use the same algorithms that are used for finding edit distance in strings to find edit distances in sentences. You can think of a sentence as a string drawn from an alphabet where each character is a word in the English language (assuming that spaces are used to mark where one "character" starts and the next ends). Any standard algorithm for computing edit distance, such as the standard dynamic programming approach for computing Levenshtein distance, can be adapted to solve this problem.

**Edit Distance and Jaccard Distance Calculation with NLTK ,** In information theory, linguistics and computer science, the Levenshtein distance is a string deletions or substitutions) required to change one word into the other. Multi-document summarization · Sentence extraction · Text simplification. Edit distance is a simple and effective way to measure the transposition between two word. It is case sensitive. Edit distance can be applied to the correction of spelling error or OCR error. In my case, tolerance error is 2. If the true label is “edward”, I will still accept the OCR result if it is “edweed”.

In general, this is called the sequence alignment problem. Actually it does not matter what entities you align - bits, characters, words, or DNA bases - as long as the algorithm works for one type of items it will work for everything else. What matters is whether you want *global* or *local* alignment.

**Global alignment**, which attempt to align every residue in every sequence, is most useful when the sequences are similar and of roughly equal size. A general global alignment technique is the Needleman-Wunsch algorithm algorithm, which is based on dynamic programming. When people talk about Levinstain distance they usually mean global alignment. The algorithm is so straightforward, that several people discovered it independently, and sometimes you may come across Wagner-Fischer algorithm which is essentially the same thing, but is mentioned more often in the context of edit distance between two strings of characters.

**Local alignment** is more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith-Waterman algorithm is a general local alignment method also based on dynamic programming. It is quite rarely used in natural language processing, and more often - in bioinformatics.

**Levenshtein distance,** Is there an algorithm that lets you find the word-level edit distance between 2 sentences? For eg., "A Big Fat Dog" and "The Big House with the Fat Dog" have 1 Then there has been a little more fine tuning by introducing edit distance approach to it, which is termed as Word Movers’ Distance. It comes from the paper “From Word Embeddings To Document Distances” published in EMNLP’14. Here we take minimum distance of each word from sentence 1 to sentence 2 and add them. Like:

Here is a sample implementation of the @templatetypedef's idea in ActionScript (it worked great for me), which calculates the normalized Levenshtein distance (or in other words gives a value in the range [0..1])

private function nlevenshtein(s1:String, s2:String):Number { var tokens1:Array = s1.split(" "); var tokens2:Array = s2.split(" "); const len1:uint = tokens1.length, len2:uint = tokens2.length; var d:Vector.<Vector.<uint> >=new Vector.<Vector.<uint> >(len1+1); for(i=0; i<=len1; ++i) d[i] = new Vector.<uint>(len2+1); d[0][0]=0; var i:int; var j:int; for(i=1; i<=len1; ++i) d[i][0]=i; for(i=1; i<=len2; ++i) d[0][i]=i; for(i = 1; i <= len1; ++i) for(j = 1; j <= len2; ++j) d[i][j] = Math.min( Math.min(d[i - 1][j] + 1,d[i][j - 1] + 1), d[i - 1][j - 1] + (tokens1[i - 1] == tokens2[j - 1] ? 0 : 1) ); var nlevenshteinDist:Number = (d[len1][len2]) / (Math.max(len1, len2)); return nlevenshteinDist; }

I hope this will help!

**Word-level edit distance of a sentence - string - html,** a similarity measure at word level through the nodes in accordance with the. syntactic 3.2 Sentence Similarity Based on Edit Distance. Once we have The most obvious difference is that the Edit Distance between sent1 and sent4 is 32 and the Jaccard Distance is zero, which means the Jaccard Distance algorithms sees them as identical sentence because Edit Distance depends on counting edit operations from the start to end of the string while Jaccard Distance just counts the number characters

The implementation in D is generalized over any range, and thus array. So by splitting your sentences into arrays of strings they can be run through the algorithm and an edit number will be provided.

https://dlang.org/library/std/algorithm/comparison/levenshtein_distance.html

**(PDF) Phrase Similarity through the Edit Distance,** In information theory, the levenshtein distance is a string metric for deletions or substitutions) required to change one word into the other. So Edit Distance problem has both properties (see this and this) of a dynamic programming problem. Like other typical Dynamic Programming(DP) problems, recomputations of same subproblems can be avoided by constructing a temporary array that stores results of subproblems.

Here is the Java implementation of edit distance algorithm for sentences using dynamic programming approach.

public class EditDistance { public int editDistanceDP(String sentence1, String sentence2) { String[] s1 = sentence1.split(" "); String[] s2 = sentence2.split(" "); int[][] solution = new int[s1.length + 1][s2.length + 1]; for (int i = 0; i <= s2.length; i++) { solution[0][i] = i; } for (int i = 0; i <= s1.length; i++) { solution[i][0] = i; } int m = s1.length; int n = s2.length; for (int i = 1; i <= m; i++) { for (int j = 1; j <= n; j++) { if (s1[i - 1].equals(s2[j - 1])) solution[i][j] = solution[i - 1][j - 1]; else solution[i][j] = 1 + Math.min(solution[i][j - 1], Math.min(solution[i - 1][j], solution[i - 1][j - 1])); } } return solution[s1.length][s2.length]; } public static void main(String[] args) { String sentence1 = "first second third"; String sentence2 = "second"; EditDistance ed = new EditDistance(); System.out.println("Edit Distance: " + ed.editDistanceDP(sentence1, sentence2)); } }

**The Levenshtein Algorithm,** the edit distance between them is the minimum number of edit operations required to For example, the edit distance between cat and dog is 3. heuristic is to use a version of the permuterm index, in which we omit the end-of-word symbol $. mining massive datasets optional project in hadoop framework. - ANG3L0/similar-sentence-mapreduce

**Edit distance,** Edit distance based: Algorithms falling under this category try to we can transform a sentence into tokens of words or n-grams characters. Levenshtein distance (or edit distance) between two strings is the number of deletions, insertions, or substitutions required to transform source string into target string.

**String similarity,** get from one word to the other? The less edits to be done the higher is the similarity level. This category of comparison contains the Levenshtein distance that Dan!Jurafsky! Where did the name, dynamic programming, come from? & …The 1950s were not good years for mathematical research. [the] Secretary of

**Levenshtein Distance and Text Similarity in Python,** I suggest using Levenshtein distance as a start. click for more sentences of The WER is derived from the Levenshtein distance, working at the word level For an edit distance=1 it’s 1 order of magnitude faster, for an edit distance=2 it’s 4 orders of magnitude faster, for an edit distance=3 it’s 6 orders of magnitude faster. for an edit