114 lines
3.8 KiB
Markdown
114 lines
3.8 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://pypi.org/project/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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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 ~100x
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faster than the Python implementation. The API has been carefully constructed
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so that multiple segmentations can share the underlying state to allow parallel
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usage.
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## How it works
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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 occurrences.
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For instance, provided that `choose` and `spain` occur more frequently than
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`chooses` and `pain`, and that the pair `choose spain` occurs more frequently
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than `chooses pain`, Instant Segment can help identify the domain
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`choosespain.com` as `ChooseSpain.com` which more likely matches user intent.
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We use this technique at
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[Instant Domain Search](https://instantdomainsearch.com/search/sale?q=choosespain)
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to help our users find relevant domains.
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## Using the library
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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 = "0.8.1"
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```
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### Examples
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The following examples expect `unigrams` and `bigrams` to exist. See the
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examples ([Rust](./instant-segment/examples/contrived.rs),
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[Python](./instant-segment-py/examples/contrived.py)) to see how to construct
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these objects.
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```python
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import instant_segment
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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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use instant_segment::{Search, Segmenter};
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use std::collections::HashMap;
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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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Check out the tests for more thorough examples:
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[Rust](./instant-segment/src/test_cases.rs),
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[Python](./instant-segment-py/test/test.py)
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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
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