wordcut-engine

Word segmentation library in Rust

Example

use wordcut_engine::load_dict;
use wordcut_engine::Wordcut;
use std::path::Path;

fn main() {
    let dict_path = Path::new(concat!(
        env!("CARGO_MANIFEST_DIR"),
        "/dict.txt"
    ));
    let dict = load_dict(dict_path).unwrap();
    let wordcut = Wordcut::new(dict);
    println!("{}", wordcut.put_delimiters("หมากินไก่", "|"));
}

For Evcxr

Load this project as a dependency

:dep .

Import symbols

use wordcut_engine::load_dict;
use wordcut_engine::Wordcut;
use wordcut_engine::Dict;
use std::path::Path;

Initialize

let dict: Dict = load_dict("data/thai.txt").unwrap();
let wordcut = Wordcut::new(dict);

Running

let txt = "หมากินไก่";
wordcut.put_delimiters(txt, "|")
wordcut.build_path(txt, &txt.chars().collect::<Vec<_>>())
dbg!(wordcut.build_path(txt, &txt.chars().collect::<Vec<_>>()));

Algorithm

wordcut-engine has three steps:

1. Identifying clusters, which are substrings that must not be split
2. Identifying edges of split directed acyclic graph (split-DAG); The program does not add edges that break any cluster to the graph.
3. Tokenizing a string by finding the shortest path in the split-DAG

Identifying clusters

Identifying clusters identify which substrings that must not be split.

1. Wrapping regular expressions with parentheses

For example,

[ก-ฮ]็
[ก-ฮ][่-๋]
[ก-ฮ][่-๋][ะาำ]

The above rules are wrapped with parentheses as shown below:

([ก-ฮ]็)
([ก-ฮ][่-๋])
([ก-ฮ][่-๋][ะาำ])

2. Joining regular expressions with vertical bars (|)

for example,

([ก-ฮ]็)|([ก-ฮ][่-๋])|([ก-ฮ][่-๋][ะาำ])

3. Building a DFA from the joined regular expression using regex-automata

4. Creating a directed acyclic graph (DAG) by adding edges using the DFA

5. Identifying clusters following a shortest path of a DAG from step above

Note: wordcut-engine does not allow a context sensitive rule, since it hurts the performance too much. Moreover, instead of longest matching, we use a DAG, and its shortest path to contraint cluster boundary by another cluster, therefore newmm-style context sensitive rules are not required.

Identifying split-DAG edges

In contrary to identifying clusters, identifying split-DAG edges identify what must be split. Split-DAG edge makers, wordcut-engine has three types of split-DAG edge maker, that are:

1. Dictionary-based maker
2. Rule-based maker
3. Default maker (Unk edge builder)

The dictionary-based maker traverses a prefix tree, which is particularly a trie in wordcut-engine and create an edge that matched word in the prefix tree. Rule-based maker uses regex-automata's Regex matcher built from split rules to find longest matched substrings, and add corresponding edges to the graph. wordcut-engine removes edges that break clusters. The example of split rules are shown below:

[\r\t\n ]+
[A-Za-z]+
[0-9]+
[๐-๙]+
[\(\)"'`\[\]{}\\/]

If there is no edge for each of character indice yet, a default maker create a edge that connected the known rightmost boundary.