Enum ExtractionMethod 

#[non_exhaustive]
pub enum ExtractionMethod {
    Pattern,
    Neural,
    Consensus,
    Heuristic,
    Unknown,
}

Extraction method used to identify an entity.

Research Context

Different extraction methods have different strengths:

Method	Precision	Recall	Generalization	Use Case
Pattern	Very High	Low	N/A (format-based)	Dates, emails, money
Neural	High	High	Good	General NER

See docs/ for repo-local notes and entry points.

Variants (Non-exhaustive)

Non-exhaustive enums could have additional variants added in future. Therefore, when matching against variants of non-exhaustive enums, an extra wildcard arm must be added to account for any future variants.

Pattern

Regex pattern matching (high precision for structured data like dates, money). Does not generalize - only detects format-based entities.

Neural

Neural model inference (BERT, GLiNER, etc.). The recommended default for general NER. Generalizes to unseen entities.

Consensus

Multiple methods agreed on this entity (high confidence).

Heuristic

Heuristic-based extraction (capitalization, word shape, context). Used by heuristic backends that don’t use neural models.

Unknown

Unknown or unspecified extraction method.

Implementations

impl ExtractionMethod

pub const fn is_calibrated(&self) -> bool

Returns true if this extraction method produces probabilistically calibrated confidence scores suitable for calibration analysis (ECE, Brier score, etc.).

Calibrated Methods

• Neural: Softmax outputs are intended to be probabilistic (though may need temperature scaling for true calibration)

Uncalibrated Methods

• Pattern: Binary (match/no-match); confidence is typically hardcoded
• Heuristic: Arbitrary scores from hand-crafted rules
• Consensus: Agreement count, not a probability

Example

use anno_core::ExtractionMethod;

assert!(ExtractionMethod::Neural.is_calibrated());
assert!(!ExtractionMethod::Pattern.is_calibrated());
assert!(!ExtractionMethod::Heuristic.is_calibrated());

pub const fn confidence_interpretation(&self) -> &'static str

Returns the confidence interpretation for this extraction method.

This helps users understand what the confidence score means:

• "probability": Score approximates P(correct)
• "heuristic_score": Score is a non-probabilistic quality measure
• "binary": Score is 0 or 1 (or a fixed value for matches)

Trait Implementations

impl Clone for ExtractionMethod

fn clone(&self) -> ExtractionMethod

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for ExtractionMethod

impl Debug for ExtractionMethod

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ExtractionMethod

fn default() -> ExtractionMethod

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for ExtractionMethod

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for ExtractionMethod

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Eq for ExtractionMethod

impl Hash for ExtractionMethod

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for ExtractionMethod

fn eq(&self, other: &ExtractionMethod) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl Serialize for ExtractionMethod

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for ExtractionMethod