Remove unused syntax iterators (now in language data)

spacy/syntax/iterators.pyx

@@ -1,144 +0,0 @@
-# coding: utf-8
-from __future__ import unicode_literals
-
-from ..parts_of_speech cimport NOUN, PROPN, PRON, VERB, AUX
-
-
-def english_noun_chunks(obj):
-    """
-    Detect base noun phrases from a dependency parse.
-    Works on both Doc and Span.
-    """
-    labels = ['nsubj', 'dobj', 'nsubjpass', 'pcomp', 'pobj',
-              'attr', 'ROOT']
-    doc = obj.doc # Ensure works on both Doc and Span.
-    np_deps = [doc.vocab.strings.add(label) for label in labels]
-    conj = doc.vocab.strings.add('conj')
-    np_label = doc.vocab.strings.add('NP')
-    seen = set()
-    for i, word in enumerate(obj):
-        if word.pos not in (NOUN, PROPN, PRON):
-            continue
-        # Prevent nested chunks from being produced
-        if word.i in seen:
-            continue
-        if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.i+1))
-            yield word.left_edge.i, word.i+1, np_label
-        elif word.dep == conj:
-            head = word.head
-            while head.dep == conj and head.head.i < head.i:
-                head = head.head
-            # If the head is an NP, and we're coordinated to it, we're an NP
-            if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.i+1))
-                yield word.left_edge.i, word.i+1, np_label
-
-
-# this iterator extracts spans headed by NOUNs starting from the left-most
-# syntactic dependent until the NOUN itself
-# for close apposition and measurement construction, the span is sometimes
-# extended to the right of the NOUN
-# example: "eine Tasse Tee" (a cup (of) tea) returns "eine Tasse Tee" and not
-# just "eine Tasse", same for "das Thema Familie"
-def german_noun_chunks(obj):
-    labels = ['sb', 'oa', 'da', 'nk', 'mo', 'ag', 'ROOT', 'root', 'cj', 'pd', 'og', 'app']
-    doc = obj.doc # Ensure works on both Doc and Span.
-    np_label = doc.vocab.strings.add('NP')
-    np_deps = set(doc.vocab.strings.add(label) for label in labels)
-    close_app = doc.vocab.strings.add('nk')
-
-    rbracket = 0
-    for i, word in enumerate(obj):
-        if i < rbracket:
-            continue
-        if word.pos in (NOUN, PROPN, PRON) and word.dep in np_deps:
-            rbracket = word.i+1
-            # try to extend the span to the right
-            # to capture close apposition/measurement constructions
-            for rdep in doc[word.i].rights:
-                if rdep.pos in (NOUN, PROPN) and rdep.dep == close_app:
-                    rbracket = rdep.i+1
-            yield word.left_edge.i, rbracket, np_label
-
-
-def es_noun_chunks(obj):
-    doc = obj.doc
-    np_label = doc.vocab.strings['NP']
-    left_labels = ['det', 'fixed', 'neg'] #['nunmod', 'det', 'appos', 'fixed']
-    right_labels = ['flat', 'fixed', 'compound', 'neg']
-    stop_labels = ['punct']
-    np_left_deps = [doc.vocab.strings[label] for label in left_labels]
-    np_right_deps = [doc.vocab.strings[label] for label in right_labels]
-    stop_deps = [doc.vocab.strings[label] for label in stop_labels]
-
-    def next_token(token):
-        try:
-            return token.nbor()
-        except:
-            return None
-
-    def noun_bounds(root):
-        def is_verb_token(token):
-            return token.pos in [VERB, AUX]
-
-        left_bound = root
-        for token in reversed(list(root.lefts)):
-            if token.dep in np_left_deps:
-                left_bound = token
-        right_bound = root
-        for token in root.rights:
-            if (token.dep in np_right_deps):
-                left, right = noun_bounds(token)
-                if list(filter(lambda t: is_verb_token(t) or t.dep in stop_deps,
-                               doc[left_bound.i: right.i])):
-                    break
-                else:
-                    right_bound = right
-        return left_bound, right_bound
-
-    token = doc[0]
-    while token and token.i < len(doc):
-        if token.pos in [PROPN, NOUN, PRON]:
-            left, right = noun_bounds(token)
-            yield left.i, right.i+1, np_label
-            token = right
-        token = next_token(token)
-
-
-def french_noun_chunks(obj):
-    labels = ['nsubj', 'nsubj:pass', 'obj', 'iobj', 'ROOT', 'appos', 'nmod', 'nmod:poss']
-    doc = obj.doc  # Ensure works on both Doc and Span.
-    np_deps = [doc.vocab.strings[label] for label in labels]
-    conj = doc.vocab.strings.add('conj')
-    np_label = doc.vocab.strings.add('NP')
-    seen = set()
-    for i, word in enumerate(obj):
-        if word.pos not in (NOUN, PROPN, PRON):
-            continue
-        # Prevent nested chunks from being produced
-        if word.i in seen:
-            continue
-        if word.dep in np_deps:
-            if any(w.i in seen for w in word.subtree):
-                continue
-            seen.update(j for j in range(word.left_edge.i, word.right_edge.i+1))
-            yield word.left_edge.i, word.right_edge.i+1, np_label
-        elif word.dep == conj:
-            head = word.head
-            while head.dep == conj and head.head.i < head.i:
-                head = head.head
-            # If the head is an NP, and we're coordinated to it, we're an NP
-            if head.dep in np_deps:
-                if any(w.i in seen for w in word.subtree):
-                    continue
-                seen.update(j for j in range(word.left_edge.i, word.right_edge.i+1))
-                yield word.left_edge.i, word.right_edge.i+1, np_label
-
-
-CHUNKERS = {'en': english_noun_chunks, 'de': german_noun_chunks,
-            'es': es_noun_chunks, 'fr': french_noun_chunks}