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131 lines
11 KiB
Markdown
131 lines
11 KiB
Markdown
---
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title: GoldParse
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teaser: A collection for training annotations
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tag: class
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source: spacy/gold.pyx
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---
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## GoldParse.\_\_init\_\_ {#init tag="method"}
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Create a `GoldParse`. Unlike annotations in `entities`, label annotations in
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`cats` can overlap, i.e. a single word can be covered by multiple labelled
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spans. The [`TextCategorizer`](/api/textcategorizer) component expects true
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examples of a label to have the value `1.0`, and negative examples of a label to
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have the value `0.0`. Labels not in the dictionary are treated as missing – the
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gradient for those labels will be zero.
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| Name | Type | Description |
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| ----------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `doc` | `Doc` | The document the annotations refer to. |
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| `words` | iterable | A sequence of unicode word strings. |
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| `tags` | iterable | A sequence of strings, representing tag annotations. |
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| `heads` | iterable | A sequence of integers, representing syntactic head offsets. |
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| `deps` | iterable | A sequence of strings, representing the syntactic relation types. |
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| `entities` | iterable | A sequence of named entity annotations, either as BILUO tag strings, or as `(start_char, end_char, label)` tuples, representing the entity positions. |
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| `cats` | dict | Labels for text classification. Each key in the dictionary may be a string or an int, or a `(start_char, end_char, label)` tuple, indicating that the label is applied to only part of the document (usually a sentence). |
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| **RETURNS** | `GoldParse` | The newly constructed object. |
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## GoldParse.\_\_len\_\_ {#len tag="method"}
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Get the number of gold-standard tokens.
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| Name | Type | Description |
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| ----------- | ---- | ----------------------------------- |
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| **RETURNS** | int | The number of gold-standard tokens. |
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## GoldParse.is_projective {#is_projective tag="property"}
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Whether the provided syntactic annotations form a projective dependency tree.
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| Name | Type | Description |
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| ----------- | ---- | ----------------------------------------- |
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| **RETURNS** | bool | Whether annotations form projective tree. |
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## Attributes {#attributes}
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| Name | Type | Description |
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| --------------------------------- | ---- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `tags` | list | The part-of-speech tag annotations. |
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| `heads` | list | The syntactic head annotations. |
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| `labels` | list | The syntactic relation-type annotations. |
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| `ents` | list | The named entity annotations. |
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| `cand_to_gold` | list | The alignment from candidate tokenization to gold tokenization. |
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| `gold_to_cand` | list | The alignment from gold tokenization to candidate tokenization. |
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| `cats` <Tag variant="new">2</Tag> | list | Entries in the list should be either a label, or a `(start, end, label)` triple. The tuple form is used for categories applied to spans of the document. |
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## Utilities {#util}
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### gold.biluo_tags_from_offsets {#biluo_tags_from_offsets tag="function"}
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Encode labelled spans into per-token tags, using the
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[BILUO scheme](/api/annotation#biluo) (Begin, In, Last, Unit, Out). Returns a
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list of unicode strings, describing the tags. Each tag string will be of the
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form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of
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`"B"`, `"I"`, `"L"`, `"U"`. The string `"-"` is used where the entity offsets
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don't align with the tokenization in the `Doc` object. The training algorithm
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will view these as missing values. `O` denotes a non-entity token. `B` denotes
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the beginning of a multi-token entity, `I` the inside of an entity of three or
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more tokens, and `L` the end of an entity of two or more tokens. `U` denotes a
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single-token entity.
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> #### Example
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>
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> ```python
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> from spacy.gold import biluo_tags_from_offsets
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>
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> doc = nlp(u"I like London.")
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> entities = [(7, 13, "LOC")]
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> tags = biluo_tags_from_offsets(doc, entities)
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> assert tags == ["O", "O", "U-LOC", "O"]
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> ```
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| Name | Type | Description |
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| ----------- | -------- | ----------------------------------------------------------------------------------------------------------------------------------------------- |
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| `doc` | `Doc` | The document that the entity offsets refer to. The output tags will refer to the token boundaries within the document. |
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| `entities` | iterable | A sequence of `(start, end, label)` triples. `start` and `end` should be character-offset integers denoting the slice into the original string. |
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| **RETURNS** | list | Unicode strings, describing the [BILUO](/api/annotation#biluo) tags. |
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### gold.offsets_from_biluo_tags {#offsets_from_biluo_tags tag="function"}
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Encode per-token tags following the [BILUO scheme](/api/annotation#biluo) into
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entity offsets.
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> #### Example
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>
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> ```python
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> from spacy.gold import offsets_from_biluo_tags
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>
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> doc = nlp(u"I like London.")
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> tags = ["O", "O", "U-LOC", "O"]
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> entities = offsets_from_biluo_tags(doc, tags)
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> assert entities == [(7, 13, "LOC")]
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> ```
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| Name | Type | Description |
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| ----------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `doc` | `Doc` | The document that the BILUO tags refer to. |
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| `entities` | iterable | A sequence of [BILUO](/api/annotation#biluo) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
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| **RETURNS** | list | A sequence of `(start, end, label)` triples. `start` and `end` will be character-offset integers denoting the slice into the original string. |
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### gold.spans_from_biluo_tags {#spans_from_biluo_tags tag="function" new="2.1"}
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Encode per-token tags following the [BILUO scheme](/api/annotation#biluo) into
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[`Span`](/api/span) objects. This can be used to create entity spans from
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token-based tags, e.g. to overwrite the `doc.ents`.
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> #### Example
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>
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> ```python
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> from spacy.gold import offsets_from_biluo_tags
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>
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> doc = nlp(u"I like London.")
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> tags = ["O", "O", "U-LOC", "O"]
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> doc.ents = spans_from_biluo_tags(doc, tags)
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> ```
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| Name | Type | Description |
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| ----------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `doc` | `Doc` | The document that the BILUO tags refer to. |
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| `entities` | iterable | A sequence of [BILUO](/api/annotation#biluo) tags with each tag describing one token. Each tag string will be of the form of either `""`, `"O"` or `"{action}-{label}"`, where action is one of `"B"`, `"I"`, `"L"`, `"U"`. |
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| **RETURNS** | list | A sequence of `Span` objects with added entity labels. |
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