lrtc: Low-resource text classification

This crate is a Rust implementation of the low-resource text classification method introduced in Jiang et al. (2023). Unlike Jiang et al., this implementation currently relies on zstd, rather than gzip, as a compression utility.

let training = vec!["some normal sentence".to_string(), "godzilla ate mars in June".into(),];
let training_labels = vec!["normal".to_string(), "godzilla".into(),];
let queries = vec!["another normal sentence".to_string(), "godzilla eats marshes in August".into(),];
// Using a compression level of 3, and 1 nearest neighbor:
println!("{:?}", classify(training, training_labels, queries, 3i32, 1usize));

This method seems to perform decently well for relatively sparse training sets, and does not require the same amount of tuning as neural net methods.

use csv::Reader;
use lrtc::classify;
use std::fs::File;
use std::vec::Vec;
let imdb = File::open("./data/imdb.csv").unwrap();
let mut reader = Reader::from_reader(imdb);

let mut content = Vec::with_capacity(50000);
let mut label = Vec::with_capacity(50000);
for record in reader.records() {
    content.push(record.as_ref().unwrap()[0].to_string());
    label.push(record.unwrap()[1].to_string());
}

let predictions = classify(
    content[0..10000].to_vec(),
    label[0..10000].to_vec(),
    content[40000..50000].to_vec(),
    3i32,
    5usize,
);
let correct = predictions
    .iter()
    .zip(label[40000..50000].to_vec().iter())
    .filter(|(a, b)| a == b)
    .count();

println!("{}", correct as f64 / 10000f64)
// 0.701

References

Zhiying Jiang, Matthew Yang, Mikhail Tsirlin, Raphael Tang, Yiqin Dai, and Jimmy Lin. 2023. “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6810–6828, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.findings-acl.426