What if you could instantly find hidden words in a jumble of letters without guessing?
Why Word Break Problem in DSA Go?
Imagine you have a long sentence without spaces, like a secret code, and you want to find out if it can be split into real words from a dictionary.
Trying every possible way to split the sentence by hand is slow and confusing. You might miss some splits or repeat the same checks many times, making it very tiring and error-prone.
The Word Break Problem uses a smart way to check if the sentence can be split into dictionary words by remembering past results. This saves time and avoids repeated work.
func canSplitManually(s string, dict []string) bool {
// Try all splits manually, very slow
for i := 1; i < len(s); i++ {
left := s[:i]
right := s[i:]
// Check if left and right are in dict
}
return false
}func wordBreak(s string, dict map[string]bool) bool {
dp := make([]bool, len(s)+1)
dp[0] = true
for i := 1; i <= len(s); i++ {
for j := 0; j < i; j++ {
if dp[j] && dict[s[j:i]] {
dp[i] = true
break
}
}
}
return dp[len(s)]
}This concept lets us quickly decide if a long string can be split into valid words, enabling fast text processing and language understanding.
When you type without spaces on your phone, the system can guess where to put spaces to form real words, making typing easier and faster.
Manual splitting is slow and repeats work.
Word Break uses memory to avoid repeated checks.
It helps in fast text and language processing.
Practice
s and a dictionary of words wordDict?Solution
Step 1: Understand the problem statement
The Word Break Problem asks if the entire stringscan be split into valid words from the dictionary without leftover characters.Step 2: Differentiate from similar concepts
It is not enough forsto contain a substring or be equal to a dictionary word; the whole string must be fully segmented.Final Answer:
Ifscan be segmented into a sequence of dictionary words -> Option AQuick Check:
Word Break = Full segmentation check [OK]
- Thinking partial substring match is enough
- Confusing equality with segmentation
- Ignoring leftover characters after segmentation
Solution
Step 1: Identify correct parameter types
The input stringsshould be a string, and the dictionarywordDictshould be a slice of strings.Step 2: Identify correct return type
The function returns a boolean indicating if segmentation is possible.Final Answer:
func wordBreak(s string, wordDict []string) bool -> Option BQuick Check:
Input: string + []string, Output: bool [OK]
- Swapping parameter types
- Returning wrong data type
- Using map instead of slice without reason
func main() {
s := "leetcode"
wordDict := []string{"leet", "code"}
fmt.Println(wordBreak(s, wordDict))
}
func wordBreak(s string, wordDict []string) bool {
dp := make([]bool, len(s)+1)
dp[0] = true
for i := 1; i <= len(s); i++ {
for j := 0; j < i; j++ {
if dp[j] && contains(wordDict, s[j:i]) {
dp[i] = true
break
}
}
}
return dp[len(s)]
}
func contains(dict []string, word string) bool {
for _, w := range dict {
if w == word {
return true
}
}
return false
}Solution
Step 1: Understand the dynamic programming approach
The code uses a boolean slicedpwheredp[i]is true ifs[:i]can be segmented into dictionary words.Step 2: Trace the example string "leetcode"
"leet" and "code" are in the dictionary, sodp[len(s)]becomes true.Final Answer:
true -> Option AQuick Check:
DP segmentation = true [OK]
- Forgetting to break inner loop after true
- Incorrect substring slicing
- Not initializing dp[0] to true
func wordBreak(s string, wordDict []string) bool {
dp := make([]bool, len(s))
dp[0] = true
for i := 1; i <= len(s); i++ {
for j := 0; j < i; j++ {
if dp[j] && contains(wordDict, s[j:i]) {
dp[i] = true
break
}
}
}
return dp[len(s)]
}Solution
Step 1: Check dp slice size and initialization
The dp slice must be of sizelen(s)+1to include empty substring at index 0, and dp[0] should be true.Step 2: Verify loop bounds and dp usage
Loops use indices up tolen(s), so dp must have lengthlen(s)+1to avoid index out of range.Final Answer:
dp slice length should be len(s)+1 and dp[0] initialization is correct -> Option CQuick Check:
dp size = len(s)+1 [OK]
- Using dp length as len(s) causes index errors
- Not initializing dp[0] to true
- Incorrect loop ranges causing panic
s = "applepenapple" and dictionary wordDict = ["apple", "pen"], which of the following modifications to the Word Break algorithm will correctly handle repeated words without extra space complexity?Solution
Step 1: Understand repeated word handling
Repeated words cause repeated substring checks; memoization stores results to avoid recomputation.Step 2: Analyze options for efficiency
Memoization reduces time without increasing space beyond needed; queue or sorting do not directly optimize repeated checks; single boolean loses needed state.Final Answer:
Use a memoization map to store results of substring checks to avoid recomputation -> Option DQuick Check:
Memoization = efficient repeated checks [OK]
- Ignoring repeated substring computations
- Using single boolean loses segmentation info
- Assuming sorting dictionary helps substring checks
Ready to prove it?
Pass the test and this pattern moves unknown
