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Adding noun_chunks to the Swedish language model (sv) (#4422)
* Create syntax_iterators.py Replica of spacy/lang/fr/syntax_iterators.py * Added import statements for SYNTAX_ITERATORS * Create gustavengstrom.md * Added "dobj" to list of labels in noun_chunks method and a test_noun_chunks method to the Swedish language model. * Delete README-checkpoint.md Co-authored-by: Gustav <gustav@davcon.se> Co-authored-by: Ines Montani <ines@ines.io>
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Ines Montani
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Gustav Engström |
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| Company name (if applicable) | Davcon |
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| Title or role (if applicable) | Data scientis |
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| Date | 2019-10-10 |
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| GitHub username | gustavengstrom |
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| Website (optional) | |
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@ -14,6 +14,7 @@ from ..norm_exceptions import BASE_NORMS
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from ...language import Language
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from ...attrs import LANG, NORM
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from ...util import update_exc, add_lookups
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from .syntax_iterators import SYNTAX_ITERATORS
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class SwedishDefaults(Language.Defaults):
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@ -29,6 +30,7 @@ class SwedishDefaults(Language.Defaults):
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suffixes = TOKENIZER_SUFFIXES
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stop_words = STOP_WORDS
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morph_rules = MORPH_RULES
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syntax_iterators = SYNTAX_ITERATORS
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class Swedish(Language):
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@ -0,0 +1,50 @@
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# coding: utf8
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from __future__ import unicode_literals
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from ...symbols import NOUN, PROPN, PRON
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def noun_chunks(obj):
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"""
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Detect base noun phrases from a dependency parse. Works on both Doc and Span.
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"""
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labels = [
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"nsubj",
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"nsubj:pass",
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"dobj",
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"obj",
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"iobj",
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"ROOT",
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"appos",
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"nmod",
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"nmod:poss",
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]
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doc = obj.doc # Ensure works on both Doc and Span.
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np_deps = [doc.vocab.strings[label] for label in labels]
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conj = doc.vocab.strings.add("conj")
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np_label = doc.vocab.strings.add("NP")
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seen = set()
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for i, word in enumerate(obj):
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if word.pos not in (NOUN, PROPN, PRON):
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continue
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# Prevent nested chunks from being produced
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if word.i in seen:
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continue
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if word.dep in np_deps:
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if any(w.i in seen for w in word.subtree):
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continue
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seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
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yield word.left_edge.i, word.right_edge.i + 1, np_label
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elif word.dep == conj:
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head = word.head
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while head.dep == conj and head.head.i < head.i:
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head = head.head
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# If the head is an NP, and we're coordinated to it, we're an NP
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if head.dep in np_deps:
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if any(w.i in seen for w in word.subtree):
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continue
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seen.update(j for j in range(word.left_edge.i, word.right_edge.i + 1))
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yield word.left_edge.i, word.right_edge.i + 1, np_label
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SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}
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@ -0,0 +1,48 @@
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# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.lang.sv.syntax_iterators import SYNTAX_ITERATORS
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from ...util import get_doc
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SV_NP_TEST_EXAMPLES = [
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(
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"En student läste en bok", # A student read a book
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["DET", "NOUN", "VERB", "DET", "NOUN"],
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["det", "nsubj", "ROOT", "det", "dobj"],
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[1, 1, 0, 1, -2],
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["En student", "en bok"],
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),
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(
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"Studenten läste den bästa boken.", # The student read the best book
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["NOUN", "VERB", "DET", "ADJ", "NOUN", "PUNCT"],
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["nsubj", "ROOT", "det", "amod", "dobj", "punct"],
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[1, 0, 2, 1, -3, -4],
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["Studenten", "den bästa boken"],
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),
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(
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"De samvetslösa skurkarna hade stulit de största juvelerna på söndagen", # The remorseless crooks had stolen the largest jewels that sunday
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["DET", "ADJ", "NOUN", "VERB", "VERB", "DET", "ADJ", "NOUN", "ADP", "NOUN"],
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["det", "amod", "nsubj", "aux", "root", "det", "amod", "dobj", "case", "nmod"],
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[2, 1, 2, 1, 0, 2, 1, -3, 1, -5],
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["De samvetslösa skurkarna", "de största juvelerna", "på söndagen"],
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),
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]
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@pytest.mark.parametrize(
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"text,pos,deps,heads,expected_noun_chunks", SV_NP_TEST_EXAMPLES
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)
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def test_sv_noun_chunks(sv_tokenizer, text, pos, deps, heads, expected_noun_chunks):
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tokens = sv_tokenizer(text)
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assert len(heads) == len(pos)
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doc = get_doc(
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tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps, pos=pos
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)
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noun_chunks = list(doc.noun_chunks)
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assert len(noun_chunks) == len(expected_noun_chunks)
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for i, np in enumerate(noun_chunks):
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assert np.text == expected_noun_chunks[i]
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