Word Abbreviation

Word Abbreviation - Problem

Array String Greedy Trie Sorting Hard

Given an array of distinct strings words, return the minimal possible abbreviations for every word.

The following are the rules for a string abbreviation:

The initial abbreviation for each word is: the first character, then the number of characters in between, followed by the last character.
If more than one word shares the same abbreviation, then perform the following operation: Increase the prefix (characters in the first part) of each of their abbreviations by 1.
This operation is repeated until every abbreviation is unique.
At the end, if an abbreviation did not make a word shorter, then keep it as the original word.

Example: For words ["abcdef","abndef"], both initially abbreviated as "a4f". The sequence would be ["a4f","a4f"] → ["ab3f","ab3f"] → ["abc2f","abn2f"].

Input & Output

Example 1 — Basic Conflict Resolution

$ Input: words = ["like", "god", "internal", "me", "internet", "interval", "intension", "face", "intrusion"]

› Output: ["l2e","god","internal","me","i6t","interval","inte4n","f2e","intr4n"]

💡 Note: Words with conflicts need longer prefixes: 'internal', 'internet', 'interval', 'intension', 'intrusion' all start with 'i', so they need different prefix lengths to become unique.

Example 2 — Simple Case

$ Input: words = ["aa", "aaa"]

› Output: ["aa","aaa"]

💡 Note: Both words are too short to abbreviate meaningfully, so they remain unchanged.

Example 3 — No Conflicts

$ Input: words = ["word", "test", "sample"]

› Output: ["w2d","t2t","s4e"]

💡 Note: All words have different first characters, so simple abbreviation with 1-character prefix works for all.

Constraints

1 ≤ words.length ≤ 400
2 ≤ words[i].length ≤ 400
words[i] consists of lowercase English letters
All the strings of words are unique

Visualization

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Asked in

G Google 15 f Facebook 12 a Amazon 8

The key insight is to incrementally extend prefixes only for conflicting abbreviations until all become unique. The optimal approach uses a trie structure to efficiently find minimal unique prefixes in O(n × m) time and space.

Common Approaches

✓ Trie-based Approach

⏱️ Time: O(n × m) Space: O(n × m)

Build a trie of all words, then for each word find the shortest prefix that uniquely identifies it by traversing the trie and checking when the path becomes unique.

Brute Force Simulation

⏱️ Time: O(n² × m) Space: O(n × m)

For each iteration, generate abbreviations for all words with current prefix lengths, find conflicts, and extend prefixes for conflicting groups until all abbreviations are unique.

Trie-based Approach — Algorithm Steps

Build trie from all input words
For each word, traverse trie to find minimal unique prefix
Generate abbreviation using the minimal prefix
Return original word if abbreviation is not shorter

Visualization

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Step-by-Step Walkthrough

Build Trie

Insert all words into trie with count tracking

Find Prefix

For each word, find shortest path with count = 1

Generate

Create abbreviation using minimal prefix

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#define MAX_WORDS 1000
#define MAX_LEN 100
#define ALPHABET_SIZE 26

typedef struct TrieNode {
    struct TrieNode* children[ALPHABET_SIZE];
    int count;
} TrieNode;

TrieNode* createNode() {
    TrieNode* node = (TrieNode*)malloc(sizeof(TrieNode));
    node->count = 0;
    for (int i = 0; i < ALPHABET_SIZE; i++) {
        node->children[i] = NULL;
    }
    return node;
}

char result[MAX_WORDS][MAX_LEN];
char words[MAX_WORDS][MAX_LEN];

void solution(int n) {
    // Build trie
    TrieNode* root = createNode();
    for (int i = 0; i < n; i++) {
        TrieNode* node = root;
        for (int j = 0; words[i][j]; j++) {
            int index = words[i][j] - 'a';
            if (node->children[index] == NULL) {
                node->children[index] = createNode();
            }
            node = node->children[index];
            node->count++;
        }
    }
    
    for (int i = 0; i < n; i++) {
        // Find minimal unique prefix
        TrieNode* node = root;
        int prefixLen = 0;
        int wordLen = strlen(words[i]);
        
        for (int j = 0; j < wordLen; j++) {
            int index = words[i][j] - 'a';
            node = node->children[index];
            prefixLen = j + 1;
            if (node->count == 1) {
                break;
            }
        }
        
        // Generate abbreviation
        if (wordLen <= prefixLen + 1) {
            strcpy(result[i], words[i]);
        } else {
            int middleCount = wordLen - prefixLen - 1;
            char abbrev[MAX_LEN];
            strncpy(abbrev, words[i], prefixLen);
            abbrev[prefixLen] = '\0';
            char numStr[20];
            sprintf(numStr, "%d", middleCount);
            strcat(abbrev, numStr);
            abbrev[strlen(abbrev) + 1] = '\0';
            abbrev[strlen(abbrev)] = words[i][wordLen - 1];
            
            if (strlen(abbrev) >= wordLen) {
                strcpy(result[i], words[i]);
            } else {
                strcpy(result[i], abbrev);
            }
        }
    }
}

int main() {
    char line[10000];
    fgets(line, sizeof(line), stdin);
    
    int n = 0;
    char* start = strchr(line, '[');
    if (start) {
        start++;
        char* token = strtok(start, ",]");
        while (token && n < MAX_WORDS) {
            while (*token == ' ' || *token == '"') token++;
            char* end = token + strlen(token) - 1;
            while (end > token && (*end == ' ' || *end == '"' || *end == '\n')) end--;
            *(end + 1) = '\0';
            strcpy(words[n], token);
            n++;
            token = strtok(NULL, ",]");
        }
    }
    
    solution(n);
    
    printf("[");
    for (int i = 0; i < n; i++) {
        if (i > 0) printf(",");
        printf("\"%s\"", result[i]);
    }
    printf("]\n");
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(n × m)

Building trie takes O(n × m) where n is number of words and m is average word length, finding prefixes takes O(n × m)

✓ Linear Growth

Space Complexity

O(n × m)

Trie structure stores all characters of all words in worst case

⚡ Linearithmic Space

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Input & Output

Constraints

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Related Problems

Common Approaches

Trie-based Approach — Algorithm Steps

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Code -

Time & Space Complexity

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