1410. HTML Entity Parser

This problem involves parsing an HTML string and replacing HTML entities with their corresponding characters. The solution utilizes a hash table (or map) to efficiently map entities to characters, and then iterates through the input string to perform the replacements.

Approach

The core idea is to create a mapping of HTML entities to their corresponding characters. We then iterate through the input string, checking for the presence of these entities. If found, we replace them with their character equivalent; otherwise, we keep the character as is. This approach can be implemented in several ways:

1. Iterative Approach with Character-by-Character Comparison:

This approach directly iterates through the input string. For each character, it checks if a potential entity starts at that position. If an entity is found, it's replaced; otherwise, the character is appended to the output. This requires comparing substrings of varying lengths (up to 7 characters for ⁄).

2. Regular Expression Approach:

A more concise solution uses regular expressions. We construct a regular expression that matches any of the defined HTML entities. The replace() method, with a callback function, allows us to substitute the matched entities with their corresponding characters from our hash table. This approach is generally more efficient for larger inputs.

Time and Space Complexity Analysis

Iterative Approach:

• Time Complexity: O(n*m), where n is the length of the input string and m is the maximum length of an entity (which is a constant 7 in this case). In essence, it's linear because we visit each character of the input string at most a constant number of times.
• Space Complexity: O(n) in the worst case, where the output string has the same length as the input. The hash table used for mapping entities to characters has a constant size.

Regular Expression Approach:

• Time Complexity: The time complexity of regular expression matching can vary depending on the regex engine. In general, it's considered to be roughly linear, O(n), although it can degrade to quadratic time in the worst case for poorly written regular expressions. However, for this specific problem and the relatively simple regular expression used, the time complexity should be considered linear.
• Space Complexity: O(n) in the worst case, similar to the iterative approach. The space used by the regular expression itself is considered constant.

Code Implementation (Iterative Approach)

The following code demonstrates the iterative approach using several programming languages:

Python:

def entityParser(text: str) -> str:
    entities = {
        """: '"',
        "'": "'",
        "&": "&",
        ">": ">",
        "&lt;": "<",
        "&frasl;": "/"
    }
    result = ""
    i = 0
    while i < len(text):
        found = False
        for entity, char in entities.items():
            if text.startswith(entity, i):
                result += char
                i += len(entity)
                found = True
                break
        if not found:
            result += text[i]
            i += 1
    return result

Java:

class Solution {
    public String entityParser(String text) {
        Map<String, String> entities = Map.of(
                "&quot;", "\"",
                "&apos;", "'",
                "&amp;", "&",
                "&gt;", ">",
                "&lt;", "<",
                "&frasl;", "/"
        );
        StringBuilder result = new StringBuilder();
        int i = 0;
        while (i < text.length()) {
            boolean found = false;
            for (Map.Entry<String, String> entry : entities.entrySet()) {
                String entity = entry.getKey();
                if (text.startsWith(entity, i)) {
                    result.append(entry.getValue());
                    i += entity.length();
                    found = true;
                    break;
                }
            }
            if (!found) {
                result.append(text.charAt(i));
                i++;
            }
        }
        return result.toString();
    }
}

JavaScript:

function entityParser(text) {
    const entities = {
        """: '"',
        "'": "'",
        "&": "&",
        ">": ">",
        "&lt;": "<",
        "&frasl;": "/"
    };
    let result = "";
    let i = 0;
    while (i < text.length) {
        let found = false;
        for (const [entity, char] of Object.entries(entities)) {
            if (text.startsWith(entity, i)) {
                result += char;
                i += entity.length;
                found = true;
                break;
            }
        }
        if (!found) {
            result += text[i];
            i++;
        }
    }
    return result;
}

(Other languages like C++, Go, etc., would follow similar patterns.)

Code Implementation (Regular Expression Approach) - TypeScript Example

function entityParser(text: string): string {
    const entities = {
        """: '"',
        "'": "'",
        "&": "&",
        ">": ">",
        "&lt;": "<",
        "&frasl;": "/",
    };
 
    const regex = new RegExp(Object.keys(entities).join("|"), "g");
    return text.replace(regex, (match) => entities[match]);
}

This concisely replaces all matching entities at once. Remember to adapt the regular expression creation to your chosen language if needed. The core concept remains the same.