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"""Annif backend using Yake keyword extraction""" |
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# For license remarks of this backend see README.md: |
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# https://github.com/NatLibFi/Annif#license. |
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import yake |
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import joblib |
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import os.path |
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import re |
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from collections import defaultdict |
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from rdflib.namespace import SKOS |
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import annif.util |
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from . import backend |
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from annif.suggestion import SubjectSuggestion, ListSuggestionResult |
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from annif.exception import ConfigurationException |
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from annif.exception import NotSupportedException |
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class YakeBackend(backend.AnnifBackend): |
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"""Yake based backend for Annif""" |
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name = "yake" |
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needs_subject_index = False |
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# defaults for uninitialized instances |
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_index = None |
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_graph = None |
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INDEX_FILE = 'yake-index' |
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DEFAULT_PARAMETERS = { |
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'max_ngram_size': 4, |
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'deduplication_threshold': 0.9, |
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'deduplication_algo': 'levs', |
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'window_size': 1, |
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'num_keywords': 100, |
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'features': None, |
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'label_types': ['prefLabel', 'altLabel'], |
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'remove_parentheses': False |
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} |
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def default_params(self): |
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params = backend.AnnifBackend.DEFAULT_PARAMETERS.copy() |
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params.update(self.DEFAULT_PARAMETERS) |
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return params |
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@property |
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def is_trained(self): |
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return True |
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@property |
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def label_types(self): |
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if type(self.params['label_types']) == str: # Label types set by user |
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label_types = [lt.strip() for lt |
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in self.params['label_types'].split(',')] |
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self._validate_label_types(label_types) |
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else: |
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label_types = self.params['label_types'] # The defaults |
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return [getattr(SKOS, lt) for lt in label_types] |
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def _validate_label_types(self, label_types): |
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for lt in label_types: |
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if lt not in ('prefLabel', 'altLabel', 'hiddenLabel'): |
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raise ConfigurationException( |
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f'invalid label type {lt}', backend_id=self.backend_id) |
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def initialize(self): |
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self._initialize_index() |
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def _initialize_index(self): |
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if self._index is None: |
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path = os.path.join(self.datadir, self.INDEX_FILE) |
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if os.path.exists(path): |
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self._index = joblib.load(path) |
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self.debug( |
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f'Loaded index from {path} with {len(self._index)} labels') |
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else: |
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self.info('Creating index') |
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self._index = self._create_index() |
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self._save_index(path) |
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self.info(f'Created index with {len(self._index)} labels') |
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def _save_index(self, path): |
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annif.util.atomic_save( |
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self._index, |
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self.datadir, |
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self.INDEX_FILE, |
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method=joblib.dump) |
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def _create_index(self): |
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index = defaultdict(set) |
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skos_vocab = self.project.vocab.skos |
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for concept in skos_vocab.concepts: |
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uri = str(concept) |
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labels = skos_vocab.get_concept_labels( |
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concept, self.label_types, self.params['language']) |
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for label in labels: |
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label = self._normalize_label(label) |
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index[label].add(uri) |
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index.pop('', None) # Remove possible empty string entry |
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return dict(index) |
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def _normalize_label(self, label): |
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label = str(label) |
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if annif.util.boolean(self.params['remove_parentheses']): |
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label = re.sub(r' \(.*\)', '', label) |
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normalized_label = self._normalize_phrase(label) |
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return self._sort_phrase(normalized_label) |
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def _normalize_phrase(self, phrase): |
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normalized = [] |
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for word in phrase.split(): |
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normalized.append( |
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self.project.analyzer.normalize_word(word).lower()) |
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return ' '.join(normalized) |
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def _sort_phrase(self, phrase): |
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words = phrase.split() |
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return ' '.join(sorted(words)) |
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def _suggest(self, text, params): |
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self.debug( |
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f'Suggesting subjects for text "{text[:20]}..." (len={len(text)})') |
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limit = int(params['limit']) |
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self._kw_extractor = yake.KeywordExtractor( |
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lan=params['language'], |
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n=int(params['max_ngram_size']), |
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dedupLim=float(params['deduplication_threshold']), |
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dedupFunc=params['deduplication_algo'], |
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windowsSize=int(params['window_size']), |
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top=int(params['num_keywords']), |
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features=self.params['features']) |
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keyphrases = self._kw_extractor.extract_keywords(text) |
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suggestions = self._keyphrases2suggestions(keyphrases) |
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subject_suggestions = [SubjectSuggestion( |
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uri=uri, |
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label=None, |
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notation=None, |
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score=score) |
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for uri, score in suggestions[:limit] if score > 0.0] |
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return ListSuggestionResult.create_from_index(subject_suggestions, |
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self.project.subjects) |
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def _keyphrases2suggestions(self, keyphrases): |
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suggestions = [] |
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not_matched = [] |
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for kp, score in keyphrases: |
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uris = self._keyphrase2uris(kp) |
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for uri in uris: |
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suggestions.append( |
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(uri, self._transform_score(score))) |
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if not uris: |
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not_matched.append((kp, self._transform_score(score))) |
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# Remove duplicate uris, conflating the scores |
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suggestions = self._combine_suggestions(suggestions) |
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self.debug('Keyphrases not matched:\n' + '\t'.join( |
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[kp[0] + ' ' + str(kp[1]) for kp |
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in sorted(not_matched, reverse=True, key=lambda kp: kp[1])])) |
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return suggestions |
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def _keyphrase2uris(self, keyphrase): |
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keyphrase = self._normalize_phrase(keyphrase) |
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keyphrase = self._sort_phrase(keyphrase) |
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return self._index.get(keyphrase, []) |
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def _transform_score(self, score): |
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score = max(score, 0) |
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return 1.0 / (score + 1) |
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def _combine_suggestions(self, suggestions): |
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combined_suggestions = {} |
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for uri, score in suggestions: |
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if uri not in combined_suggestions: |
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combined_suggestions[uri] = score |
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else: |
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old_score = combined_suggestions[uri] |
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combined_suggestions[uri] = self._combine_scores( |
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score, old_score) |
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return list(combined_suggestions.items()) |
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def _combine_scores(self, score1, score2): |
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# The result is never smaller than the greater input |
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score1 = score1/2 + 0.5 |
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score2 = score2/2 + 0.5 |
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confl = score1 * score2 / (score1 * score2 + (1-score1) * (1-score2)) |
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return (confl-0.5) * 2 |
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def _train(self, corpus, params): |
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raise NotSupportedException( |
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'Training yake backend is not possible.') |
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NatLibFi /
Annif
