Source code for nlp_architect.models.absa.train.rules

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# Copyright 2017-2018 Intel Corporation
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from nlp_architect.models.absa.inference.data_types import Polarity
from nlp_architect.models.absa.train.data_types import CandidateTerm, \
    RelCategory, DepRelationTerm, POS


[docs]def rule_1(dep_rel, gov_entry, dep_entry, text): """Extract term if rule 1 applies. Args: dep_rel (DepRelation): Dependency relation. gov_entry (DicEntrySentiment): Governor opinion entry. dep_entry (DicEntrySentiment): Dependant opinion entry. text (str): Sentence text. """ candidate = None (anchor_entry, anchor, related) = (gov_entry, dep_rel.gov, dep_rel.dep) \ if gov_entry else (dep_entry, dep_rel.dep, dep_rel.gov) if related.norm_pos == POS.ADJ and dep_rel.rel.startswith('conj'): polarity = anchor_entry.polarity candidate = CandidateTerm(related, anchor, text, polarity) return candidate
[docs]def rule_2(dep_rel, relation_list, dep_entry, text): """Extract term if rule 2 applies. Args: dep_rel (DepRelation): Dependency relation. relation_list (list of DepRelation): Generic relations between all tokens. dep_entry (OpinionTerm): Dependent token. text (str): Sentence text. """ candidate = None for curr_rt in relation_list: if (curr_rt.gov, curr_rt.rel, curr_rt.dep.norm_pos) == \ (dep_rel.gov, dep_rel.rel, POS.ADJ) and curr_rt.dep != dep_rel.dep: candidate = CandidateTerm(curr_rt.dep, dep_rel.dep, text, dep_entry.polarity) return candidate
[docs]def rule_3(dep_rel, relation_list, text): """Extract term if rule 3 applies. Args: dep_rel (DepRelation): Dependency relation. relation_list (list of DepRelation): Generic relations between all tokens. text (str): Sentence text. """ candidate = None if dep_rel.gov.norm_pos == POS.NN and is_subj_obj_or_mod(dep_rel): aspect = expand_aspect(dep_rel.gov, relation_list) candidate = CandidateTerm(aspect, dep_rel.dep, text, Polarity.UNK) return candidate
[docs]def rule_4(dep_rel, relation_list, text): """Extract term if rule 4 applies. Args: dep_rel (DepRelation): Dependency relation. relation_list (list of DepRelation): Generic relations between all tokens. relation between tokens text (str): Sentence text. """ candidate = None for curr_rt in relation_list: if curr_rt.gov == dep_rel.gov and curr_rt.dep != dep_rel.dep and \ curr_rt.dep.norm_pos == POS.NN and is_subj_obj_or_mod(curr_rt) and \ is_subj_obj_or_mod(dep_rel): aspect = expand_aspect(curr_rt.dep, relation_list) candidate = CandidateTerm(aspect, dep_rel.dep, text, Polarity.UNK) return candidate
[docs]def rule_5(dep_rel, text): """Extract term if rule 5 applies. Args: dep_rel (DepRelation): Dependency relation. text (str): Sentence text. """ candidate = None if is_subj_obj_or_mod(dep_rel) and dep_rel.dep.norm_pos == POS.ADJ: return CandidateTerm(dep_rel.dep, dep_rel.gov, text, Polarity.UNK) return candidate
[docs]def rule_6(dep_rel, relation_list, text): """Extract term if rule 6 applies. Args: dep_rel (DepRelation): Dependency relation. relation_list (list of DepRelation): Generic relations between all tokens. text (str): Sentence text. """ candidate = None if dep_rel.rel in ('conj_and', 'conj_but'): aspect = expand_aspect(dep_rel.dep, relation_list) candidate = CandidateTerm(aspect, dep_rel.gov, text, Polarity.UNK) return candidate
[docs]def is_subj_obj_or_mod(rt): return any(rt.rel in cat.value for cat in (RelCategory.SUBJ, RelCategory.OBJ, RelCategory.MOD))
[docs]def expand_aspect(in_aspect_token, relation_list): """Expand aspect by Looking for a noun word that it's gov is the aspect. if it has (noun) compound relation add it to aspect.""" aspect = DepRelationTerm(text=in_aspect_token.text, lemma=in_aspect_token.lemma, pos=in_aspect_token.pos, ner=in_aspect_token.ner, idx=in_aspect_token.idx) for rel in relation_list: if (rel.rel == 'compound') and (rel.gov.idx == aspect.idx): diff_positive = aspect.idx - len(rel.dep.text) - 1 - rel.dep.idx diff_negative = rel.dep.idx - len(aspect.text) - 1 - aspect.idx if diff_positive == 0: aspect.text = rel.dep.text + ' ' + aspect.text aspect.lemma = rel.dep.text + ' ' + aspect.lemma aspect.idx = rel.dep.idx if diff_negative == 0: aspect.text = aspect.text + ' ' + rel.dep.text aspect.lemma = aspect.lemma + ' ' + rel.dep.lemma aspect.text = aspect.text.lower() aspect.lemma = aspect.lemma.lower() return aspect