# wordcut-engine
Word segmentation library in Rust
## Example
```Rust
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
```Rust
use wordcut_engine::load_dict;
use wordcut_engine::Wordcut;
use wordcut_engine::Dict;
use std::path::Path;
```
Initialize
```Rust
let dict: Dict = load_dict("data/thai.txt").unwrap();
let wordcut = Wordcut::new(dict);
```
Running
```Rust
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])
```
for example,
```
3. Building a DFA from the joined regular expression using [regex-automata](https://github.com/BurntSushi/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](https://github.com/PyThaiNLP/pythainlp/blob/dev/pythainlp/tokenize/newmm.py)-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](https://github.com/BurntSushi/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.