Matcher

A high-performance, multi-functional word matcher implemented in Rust.

Designed to solve AND OR NOT and TEXT VARIATIONS problems in word/word_list matching. For detailed implementation, see the Design Document.

Features

• Multiple Matching Methods:
  • Simple Word Matching
  • Regex-Based Matching
  • Similarity-Based Matching
• Text Normalization:
  • Fanjian: Simplify traditional Chinese characters to simplified ones. Example: 蟲艸 -> 虫艹
  • Delete: Remove specific characters. Example: *Fu&*iii&^%%*&kkkk -> Fuiiikkkk
  • Normalize: Normalize special characters to identifiable characters. Example: 𝜢𝕰𝕃𝙻Ϙ 𝙒ⓞƦℒ𝒟! -> hello world
  • PinYin: Convert Chinese characters to Pinyin for fuzzy matching. Example: 西安 -> /xi//an/, matches 洗按 -> /xi//an/, but not 先 -> /xian/
  • PinYinChar: Convert Chinese characters to Pinyin. Example: 西安 -> xian, matches 洗按 and 先 -> xian
• AND OR NOT Word Matching:
  • Takes into account the number of repetitions of words.
  • Example: hello&world matches hello world and world,hello
  • Example: 无&法&无&天 matches 无无法天 (because 无 is repeated twice), but not 无法天
  • Example: hello~helloo~hhello matches hello but not helloo and hhello
• Customizable Exemption Lists: Exclude specific words from matching.
• Efficient Handling of Large Word Lists: Optimized for performance.

Usage

Adding to Your Project

To use matcher_rs in your Rust project, run the following command:

cargo add matcher_rs

Explanation of the configuration

• Matcher's configuration is defined by the MatchTableMap = HashMap<u32, Vec<MatchTable>> type, the key of MatchTableMap is called match_id, for each match_id, the table_id inside should but isn't required to be unique.
• SimpleMatcher's configuration is defined by the SimpleMatchTableMap = HashMap<SimpleMatchType, HashMap<u32, &'a str>> type, the value HashMap<u32, &'a str>'s key is called word_id, word_id is required to be globally unique.

MatchTable

• table_id: The unique ID of the match table.
• match_table_type: The type of the match table.
• word_list: The word list of the match table.
• exemption_simple_match_type: The type of the exemption simple match.
• exemption_word_list: The exemption word list of the match table.

For each match table, word matching is performed over the word_list, and exemption word matching is performed over the exemption_word_list. If the exemption word matching result is True, the word matching result will be False.

MatchTableType

• Simple: Supports simple multiple patterns matching with text normalization defined by simple_match_type.
  • We offer transformation methods for text normalization, including Fanjian, Normalize, PinYin ···.
  • It can handle combination patterns and repeated times sensitive matching, delimited by & and ~, such as hello&world&hello will match hellohelloworld and worldhellohello, but not helloworld due to the repeated times of hello.
• Regex: Supports regex patterns matching.
  • SimilarChar: Supports similar character matching using regex.
    • ["hello,hallo,hollo,hi", "word,world,wrd,🌍", "!,?,~"] will match helloworld, hollowrd, hi🌍 ··· any combinations of the words split by , in the list.
  • Acrostic: Supports acrostic matching using regex (currently only supports Chinese and simple English sentences).
    • ["h,e,l,l,o", "你,好"] will match hope, endures, love, lasts, onward. and 你的笑容温暖, 好心情常伴。.
  • Regex: Supports regex matching.
    • ["h[aeiou]llo", "w[aeiou]rd"] will match hello, world, hillo, wurld ··· any text that matches the regex in the list.
• Similar: Supports similar text matching based on distance and threshold.
  • Levenshtein: Supports similar text matching based on Levenshtein distance.
  • DamerauLevenshtein: Supports similar text matching based on Damerau-Levenshtein distance.
  • Indel: Supports similar text matching based on Indel distance.
  • Jaro: Supports similar text matching based on Jaro distance.
  • JaroWinkler: Supports similar text matching based on Jaro-Winkler distance.

SimpleMatchType

• None: No transformation.
• Fanjian: Traditional Chinese to simplified Chinese transformation. Based on FANJIAN and UNICODE.
  • 妳好 -> 你好
  • 現⾝ -> 现身
• Delete: Delete all punctuation, special characters and white spaces.
  • hello, world! -> helloworld
  • 《你∷好》 -> 你好
• Normalize: Normalize all English character variations and number variations to basic characters. Based on UPPER_LOWER, EN_VARIATION and NUM_NORM.
  • ℋЀ⒈㈠ϕ -> he11o
  • ⒈Ƨ㊂ -> 123
• PinYin: Convert all unicode Chinese characters to pinyin with boundaries. Based on PINYIN.
  • 你好 -> ␀ni␀␀hao␀
  • 西安 -> ␀xi␀␀an␀
• PinYinChar: Convert all unicode Chinese characters to pinyin without boundaries. Based on PINYIN_CHAR.
  • 你好 -> nihao
  • 西安 -> xian

You can combine these transformations as needed. Pre-defined combinations like DeleteNormalize and FanjianDeleteNormalize are provided for convenience.

Avoid combining PinYin and PinYinChar due to that PinYin is a more limited version of PinYinChar, in some cases like xian, can be treat as two words xi and an, or only one word xian.

Delete is technologically a combination of TextDelete and WordDelete, we implement different delete methods for text and word. 'Cause we believe CN_SPECIAL and EN_SPECIAL are parts of the word, but not for text. For text_process and reduce_text_process functions, users should use TextDelete instead of WordDelete.

• WordDelete: Delete all patterns in PUNCTUATION_SPECIAL.
• TextDelete: Delete all patterns in PUNCTUATION_SPECIAL, CN_SPECIAL, EN_SPECIAL.

Basic Example

Here’s a basic example of how to use the Matcher struct for text matching:

use matcher_rs::{text_process, reduce_text_process, SimpleMatchType};

let result = text_process(SimpleMatchType::TextDelete, "你好，世界！");
let result = reduce_text_process(SimpleMatchType::FanjianDeleteNormalize, "你好，世界！");

use std::collections::HashMap;
use matcher_rs::{Matcher, MatchTableMap, MatchTable, MatchTableType, SimpleMatchType};

let match_table_map: MatchTableMap = HashMap::from_iter(vec![
    (1, vec![MatchTable {
        table_id: 1,
        match_table_type: MatchTableType::Simple { simple_match_type: SimpleMatchType::FanjianDeleteNormalize},
        word_list: vec!["example", "test"],
        exemption_simple_match_type: SimpleMatchType::FanjianDeleteNormalize,
        exemption_word_list: vec![],
    }]),
]);
let matcher = Matcher::new(&match_table_map);
let text = "This is an example text.";
let results = matcher.word_match(text);

use std::collections::HashMap;
use matcher_rs::{SimpleMatchType, SimpleMatcher};

let mut simple_match_type_word_map = HashMap::default();
let mut simple_word_map = HashMap::default();

simple_word_map.insert(1, "你好");
simple_word_map.insert(2, "世界");

simple_match_type_word_map.insert(SimpleMatchType::Fanjian, simple_word_map);

let matcher = SimpleMatcher::new(&simple_match_type_word_map);
let text = "你好，世界！";
let results = matcher.process(text);

For more detailed usage examples, please refer to the test.rs file.

Benchmarks

The matcher_rs library includes benchmarks to measure the performance of the matcher. You can find the benchmarks in the bench.rs file. To run the benchmarks, use the following command:

cargo bench

Contributing

Contributions to matcher_rs are welcome! If you find a bug or have a feature request, please open an issue on the GitHub repository. If you would like to contribute code, please fork the repository and submit a pull request.

License

matcher_rs is licensed under the MIT OR Apache-2.0 license.

More Information

For more details, visit the GitHub repository.