160 lines
4.9 KiB
Markdown
160 lines
4.9 KiB
Markdown
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# Instant Segment: fast English word segmentation in Rust
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[](https://docs.rs/instant-segment/)
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[](https://crates.io/crates/instant-segment)
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[](https://github.com/InstantDomainSearch/instant-segment/actions?query=workflow%3ACI)
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[](LICENSE-APACHE)
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```python
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segmenter = instant_segment.Segmenter(unigrams(), bigrams())
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search = instant_segment.Search()
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segmenter.segment("instantdomainsearch", search)
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print([word for word in search])
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--> ['instant', 'domain', 'search']
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```
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```rust
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let segmenter = Segmenter::from_maps(unigrams, bigrams);
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let mut search = Search::default();
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let words = segmenter
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.segment("instantdomainsearch", &mut search)
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.unwrap();
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println!("{:?}", words.collect::<Vec<&str>>())
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--> ["instant", "domain", "search"]
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```
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Instant Segment is a fast Apache-2.0 library for English word segmentation. It
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is based on the Python [wordsegment][python] project written by Grant Jenks,
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which is in turn based on code from Peter Norvig's chapter [Natural Language
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Corpus Data][chapter] from the book [Beautiful Data][book] (Segaran and
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Hammerbacher, 2009).
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The data files in this repository are derived from the [Google Web Trillion Word
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Corpus][corpus], as described by Thorsten Brants and Alex Franz, and
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[distributed][distributed] by the Linguistic Data Consortium. Note that this
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data **"may only be used for linguistic education and research"**, so for any
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other usage you should acquire a different data set.
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For the microbenchmark included in this repository, Instant Segment is ~17x
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faster than the Python implementation. Further optimizations are planned -- see
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the [issues][issues]. The API has been carefully constructed so that multiple
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segmentations can share the underlying state to allow parallel usage.
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## Installing
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### Python **(>= 3.9)**
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```sh
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pip install instant-segment
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```
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### Rust
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```toml
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[dependencies]
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instant-segment = "*"
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```
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## Using
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Instant Segment works by segmenting a string into words by selecting the splits
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with the highest probability given a corpus of words and their occurances.
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For instance, provided that `choose` and `spain` occur more frequently than
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`chooses` and `pain`, Instant Segment can help you split the string
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`choosespain.com` into
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[`ChooseSpain.com`](https://instantdomainsearch.com/search/sale?q=choosespain)
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which more likely matches user intent.
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```python
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import instant_segment
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def main():
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unigrams = []
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unigrams.append(("choose", 50))
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unigrams.append(("chooses", 10))
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unigrams.append(("spain", 50))
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unigrams.append(("pain", 10))
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bigrams = []
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bigrams.append((("choose", "spain"), 10))
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bigrams.append((("chooses", "pain"), 10))
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segmenter = instant_segment.Segmenter(iter(unigrams), iter(bigrams))
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search = instant_segment.Search()
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segmenter.segment("choosespain", search)
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print([word for word in search])
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if __name__ == "__main__":
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main()
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```
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```rust
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use instant_segment::{Search, Segmenter}; use std::collections::HashMap;
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fn main() {
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let mut unigrams = HashMap::default();
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unigrams.insert("choose".into(), 50 as f64);
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unigrams.insert("chooses".into(), 10 as f64);
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unigrams.insert("spain".into(), 50 as f64);
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unigrams.insert("pain".into(), 10 as f64);
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let mut bigrams = HashMap::default();
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bigrams.insert(("choose".into(), "spain".into()), 10 as f64);
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bigrams.insert(("chooses".into(), "pain".into()), 10 as f64);
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let segmenter = Segmenter::from_maps(unigrams, bigrams);
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let mut search = Search::default();
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let words = segmenter.segment("choosespain", &mut search).unwrap();
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println!("{:?}", words.collect::<Vec<&str>>())
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}
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```
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```
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['choose', 'spain']
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```
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Play with the examples above to see that different numbers of occurances will
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influence the results
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The example above is succinct but, in practice, you will want to load these
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words and occurances from a corpus of data like the ones we provide
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[here](./data). Check out
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[the](./instant-segment/instant-segment-py/test/test.py)
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[tests](./instant-segment/instant-segment/src/test_data.rs) to see examples of
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how you might do that.
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## Testing
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To run the tests run the following:
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```
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cargo t -p instant-segment --all-features
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```
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You can also test the python bindings with:
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```
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make test-python
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```
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[python]: https://github.com/grantjenks/python-wordsegment
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[chapter]: http://norvig.com/ngrams/
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[book]: http://oreilly.com/catalog/9780596157111/
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[corpus]:
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http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html
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[distributed]: https://catalog.ldc.upenn.edu/LDC2006T13
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[issues]: https://github.com/InstantDomainSearch/instant-segment/issues |