# nlp_architect.models.absa.train package

## Submodules

## nlp_architect.models.absa.train.acquire_terms module

class `nlp_architect.models.absa.train.acquire_terms.``AcquireTerms`(asp_thresh=3, op_thresh=2, max_iter=1)

  Bases: `object`

  Lexicon acquisition. produce opinion lexicon and an aspect lexicon based on input dataset.

  `opinion_candidate_list_curr_iter`

    candidate opinion terms in the current iteration

    Type: | dict

  `opinion_candidate_list_prev_iter`

    opinion candidates list of previous iteration

    Type: | dict

  `opinion_candidate_list`

    opinion terms learned across all iterations

    Type: | dict

  `opinion_candidates_list_final`

    final opinion candidates list

    Type: | list

  `opinion_candidate_list_raw`

    all instances of candidate opinion terms across all iterations

    Type: | dict

  `aspect_candidate_list_curr_iter`

    candidate terms in the current iteration

    Type: | dict

  `aspects_candidate_list_prev_iter`

    Aspect candidates list of previous iteration

    Type: | list

  `aspect_candidate_list`

    aspect terms learned across all iterations

    Type: | list

  `aspect_candidates_list_final`

    final aspect candidates list

    Type: | list

  `aspect_candidate_list_raw`

    all instances of candidate aspect terms across all iterations

    Type: | dict

  `ASPECT_STOP_LIST` = <nlp_architect.models.absa.train.data_types.LoadAspectStopLists object>
  `AUXILIARIES_LEX` = ['do', 'does', 'did', 'has', 'have', 'had', 'is', 'am', 'are', 'was', 'were', 'be', 'being', 'been', 'may', 'must', 'might', 'should', 'could', 'would', 'shall', 'will', 'can']
  `COLORS_LEX` = ['blue', 'black', 'brown', 'gray', 'green', 'maroon', 'orange', 'pink', 'purple', 'red', 'rose', 'turquoise', 'violet', 'white', 'yellow']
  `DETERMINERS_LEX` = ['the', 'a', 'an', 'this', 'that', 'these', 'those', 'my', 'your', 'his', 'her', 'its', 'our', 'their', 'much', 'many', 'few', 'a lot of', 'most', 'some', 'any', 'enough', 'all', 'both', 'half', 'either', 'neither', 'each', 'every', 'other', 'another', 'such', 'what', 'rather', 'quite', 'one', 'which']
  `FILTER_PATTERNS` = [re.compile('.*\\d+.*')]
  `FLOAT_FORMAT` = '{0:.3g}'
  `GENERAL_ADJECTIVES_LEX` = ['same', 'full', 'generalpurpose', 'general', 'generic', 'unknown', 'valid', 'back', 'typical', 'available', 'internal', 'just', 'known', 'natural', 'only', 'ordinary', 'physical', 'regular', 'relevant', 'other', 'like', 'first', 'about', 'dead', 'against', 'own', 'scientific', 'good', 'bad', 'national', 'moroever', 'my', 'nationwide', 'neutral', 'nonprofit', 'normal', 'operational', 'opposed', 'out', 'outside', 'overall', 'please', 'plus', 'rival', 'new', 'single', 'final', 'female', 'human', 'original', 'old', 'mere', 'sure', 'open', 'technical', 'related', 'secondary', 'last', 'far', 'personal', 'online', 'concrete', 'incoming', 'social', 'possible', 'mobile', 'able', 'male', 'casual', 'formal', 'medical', 'pevious', 'logical', 'different', 'long', 'double', 'top', 'actual', 'half', 'usual', 'specific']
  `GENERIC_OPINION_LEX` = ['abnormal', 'abolish', 'abominable', 'abominably', 'abominate', 'abomination', 'abort', 'aborted', 'aborts', 'abound', 'abounds', 'abrade', 'abrasive', 'abrupt', 'abruptly', 'abscond', 'absence', 'absentee', 'absurd', 'absurdity', 'absurdly', 'absurdness', 'abundance', 'abundant', 'abuse', 'abused', 'abuses', 'abusive', 'abysmal', 'abysmally', 'abyss', 'accessable', 'accessible', 'accidental', 'acclaim', 'acclaimed', 'acclamation', 'accolade', 'accolades', 'accommodative', 'accomodative', 'accomplish', 'accomplished', 'accomplishment', 'accomplishments', 'accost', 'accurate', 'accurately', 'accursed', 'accusation', 'accusations', 'accuse', 'accuses', 'accusing', 'accusingly', 'acerbate', 'acerbic', 'acerbically', 'ache', 'ached', 'aches', 'achey', 'achievable', 'achievement', 'achievements', 'achievible', 'aching', 'acrid', 'acridly', 'acridness', 'acrimonious', 'acrimoniously', 'acrimony', 'acumen', 'adamant', 'adamantly', 'adaptable', 'adaptive', 'addict', 'addicted', 'addicting', 'addicts', 'adequate', 'adjustable', 'admirable', 'admirably', 'admiration', 'admire', 'admirer', 'admiring', 'admiringly', 'admonish', 'admonisher', 'admonishingly', 'admonishment', 'admonition', 'adorable', 'adore', 'adored', 'adorer', 'adoring', 'adoringly', 'adroit', 'adroitly', 'adulate', 'adulation', 'adulatory', 'adulterate', 'adulterated', 'adulteration', 'adulterier', 'advanced', 'advantage', 'advantageous', 'advantageously', 'advantages', 'adventuresome', 'adventurous', 'adversarial', 'adversary', 'adverse', 'adversity', 'advocate', 'advocated', 'advocates', 'affability', 'affable', 'affably', 'affectation', 'affection', 'affectionate', 'affinity', 'affirm', 'affirmation', 'affirmative', 'afflict', 'affliction', 'afflictive', 'affluence', 'affluent', 'afford', 'affordable', 'affordably', 'affront', 'afordable', 'afraid', 'aggravate', 'aggravating', 'aggravation', 'aggression', 'aggressive', 'aggressiveness', 'aggressor', 'aggrieve', 'aggrieved', 'aggrivation', 'aghast', 'agile', 'agilely', 'agility', 'agonies', 'agonize', 'agonizing', 'agonizingly', 'agony', 'agreeable', 'agreeableness', 'agreeably', 'aground', 'ail', 'ailing', 'ailment', 'aimless', 'alarm', 'alarmed', 'alarming', 'alarmingly', 'alienate', 'alienated', 'alienation', 'all-around', 'allegation', 'allegations', 'allege', 'allergic', 'allergies', 'allergy', 'alluring', 'alluringly', 'aloof', 'altercation', 'altruistic', 'altruistically', 'amaze', 'amazed', 'amazement', 'amazes', 'amazing', 'amazingly', 'ambiguity', 'ambiguous', 'ambitious', 'ambitiously', 'ambivalence', 'ambivalent', 'ambush', 'ameliorate', 'amenable', 'amenity', 'amiability', 'amiabily', 'amiable', 'amicability', 'amicable', 'amicably', 'amiss', 'amity', 'ample', 'amply', 'amputate', 'amuse', 'amusing', 'amusingly', 'anarchism', 'anarchist', 'anarchistic', 'anarchy', 'anemic', 'angel', 'angelic', 'anger', 'angrily', 'angriness', 'angry', 'anguish', 'animosity', 'annihilate', 'annihilation', 'annoy', 'annoyance', 'annoyances', 'annoyed', 'annoying', 'annoyingly', 'annoys', 'anomalous', 'anomaly', 'antagonism', 'antagonist', 'antagonistic', 'antagonize', 'anti-', 'anti-american', 'anti-israeli', 'anti-occupation', 'anti-proliferation', 'anti-semites', 'anti-social', 'anti-us', 'anti-white', 'antipathy', 'antiquated', 'antithetical', 'anxieties', 'anxiety', 'anxious', 'anxiously', 'anxiousness', 'apathetic', 'apathetically', 'apathy', 'apocalypse', 'apocalyptic', 'apologist', 'apologists', 'apotheosis', 'appal', 'appall', 'appalled', 'appalling', 'appallingly', 'appeal', 'applaud', 'appreciable', 'appreciate', 'appreciated', 'appreciates', 'appreciation', 'appreciative', 'appreciatively', 'apprehension', 'apprehensions', 'apprehensive', 'apprehensively', 'approval', 'approve', 'arbitrary', 'arcane', 'archaic', 'ardent', 'ardently', 'ardor', 'arduous', 'arduously', 'argumentative', 'arrogance', 'arrogant', 'arrogantly', 'articulate', 'ashamed', 'asinine', 'asininely', 'asinininity', 'askance', 'asperse', 'aspersion', 'aspersions', 'aspiration', 'aspirations', 'aspire', 'assail', 'assassin', 'assassinate', 'assault', 'assult', 'assurance', 'assurances', 'assure', 'assuredly', 'assuring', 'astonish', 'astonished', 'astonishing', 'astonishingly', 'astonishment', 'astound', 'astounded', 'astounding', 'astoundingly', 'astray', 'astutely', 'asunder', 'atrocious', 'atrocities', 'atrocity', 'atrophy', 'attack', 'attacks', 'attentive', 'attraction', 'attractive', 'attractively', 'attune', 'audacious', 'audaciously', 'audaciousness', 'audacity', 'audible', 'audibly', 'audiciously', 'auspicious', 'austere', 'authentic', 'authoritarian', 'authoritative', 'autocrat', 'autocratic', 'autonomous', 'available', 'avalanche', 'avarice', 'avaricious', 'avariciously', 'avenge', 'aver', 'averse', 'aversion', 'avid', 'avidly', 'award', 'awarded', 'awards', 'awe', 'awed', 'aweful', 'awesome', 'awesomely', 'awesomeness', 'awestruck', 'awful', 'awfully', 'awfulness', 'awkward', 'awkwardness', 'awsome', 'ax', 'babble', 'back-logged', 'back-wood', 'back-woods', 'backache', 'backaches', 'backaching', 'backbite', 'backbiting', 'backbone', 'backward', 'backwardness', 'backwood', 'backwoods', 'bad', 'badly', 'baffle', 'baffled', 'bafflement', 'baffling', 'bait', 'balanced', 'balk', 'banal', 'banalize', 'bane', 'banish', 'banishment', 'bankrupt', 'barbarian', 'barbaric', 'barbarically', 'barbarity', 'barbarous', 'barbarously', 'bargain', 'barren', 'baseless', 'bash', 'bashed', 'bashful', 'bashing', 'bastard', 'bastards', 'battered', 'battering', 'batty', 'bearish', 'beastly', 'beauteous', 'beautiful', 'beautifullly', 'beautifully', 'beautify', 'beauty', 'beckon', 'beckoned', 'beckoning', 'beckons', 'bedlam', 'bedlamite', 'befoul', 'beg', 'beggar', 'beggarly', 'begging', 'beguile', 'belabor', 'belated', 'beleaguer', 'belie', 'believable', 'believeable', 'belittle', 'belittled', 'belittling', 'bellicose', 'belligerence', 'belligerent', 'belligerently', 'beloved', 'bemoan', 'bemoaning', 'bemused', 'benefactor', 'beneficent', 'beneficial', 'beneficially', 'beneficiary', 'benefit', 'benefits', 'benevolence', 'benevolent', 'benifits', 'bent', 'berate', 'bereave', 'bereavement', 'bereft', 'berserk', 'beseech', 'beset', 'besiege', 'besmirch', 'best', 'best-known', 'best-performing', 'best-selling', 'bestial', 'betray', 'betrayal', 'betrayals', 'betrayer', 'betraying', 'betrays', 'better', 'better-known', 'better-than-expected', 'beutifully', 'bewail', 'beware', 'bewilder', 'bewildered', 'bewildering', 'bewilderingly', 'bewilderment', 'bewitch', 'bias', 'biased', 'biases', 'bicker', 'bickering', 'bid-rigging', 'bigotries', 'bigotry', 'bitch', 'bitchy', 'biting', 'bitingly', 'bitter', 'bitterly', 'bitterness', 'bizarre', 'blab', 'blabber', 'blackmail', 'blah', 'blame', 'blameless', 'blameworthy', 'bland', 'blandish', 'blaspheme', 'blasphemous', 'blasphemy', 'blasted', 'blatant', 'blatantly', 'blather', 'bleak', 'bleakly', 'bleakness', 'bleed', 'bleeding', 'bleeds', 'blemish', 'bless', 'blessing', 'blind', 'blinding', 'blindingly', 'blindside', 'bliss', 'blissful', 'blissfully', 'blister', 'blistering', 'blithe', 'bloated', 'blockage', 'blockbuster', 'blockhead', 'bloodshed', 'bloodthirsty', 'bloody', 'bloom', 'blossom', 'blotchy', 'blow', 'blunder', 'blundering', 'blunders', 'blunt', 'blur', 'bluring', 'blurred', 'blurring', 'blurry', 'blurs', 'blurt', 'boastful', 'boggle', 'bogus', 'boil', 'boiling', 'boisterous', 'bolster', 'bomb', 'bombard', 'bombardment', 'bombastic', 'bondage', 'bonkers', 'bonny', 'bonus', 'bonuses', 'boom', 'booming', 'boost', 'bore', 'bored', 'boredom', 'bores', 'boring', 'botch', 'bother', 'bothered', 'bothering', 'bothers', 'bothersome', 'boundless', 'bountiful', 'bowdlerize', 'boycott', 'braggart', 'bragger', 'brainiest', 'brainless', 'brainwash', 'brainy', 'brand-new', 'brash', 'brashly', 'brashness', 'brat', 'bravado', 'brave', 'bravery', 'bravo', 'brazen', 'brazenly', 'brazenness', 'breach', 'break', 'break-up', 'break-ups', 'breakdown', 'breaking', 'breaks', 'breakthrough', 'breakthroughs', 'breakup', 'breakups', 'breathlessness', 'breathtaking', 'breathtakingly', 'breeze', 'bribery', 'bright', 'brighten', 'brighter', 'brightest', 'brilliance', 'brilliances', 'brilliant', 'brilliantly', 'brimstone', 'brisk', 'bristle', 'brittle', 'broke', 'broken', 'broken-hearted', 'brood', 'brotherly', 'browbeat', 'bruise', 'bruised', 'bruises', 'bruising', 'brusque', 'brutal', 'brutalising', 'brutalities', 'brutality', 'brutalize', 'brutalizing', 'brutally', 'brute', 'brutish', 'bs', 'buckle', 'bug', 'bugging', 'buggy', 'bugs', 'bulkier', 'bulkiness', 'bulky', 'bulkyness', 'bull----', 'bull****', 'bullies', 'bullish', 'bullshit', 'bullshyt', 'bully', 'bullying', 'bullyingly', 'bum', 'bump', 'bumped', 'bumping', 'bumpping', 'bumps', 'bumpy', 'bungle', 'bungler', 'bungling', 'bunk', 'buoyant', 'burden', 'burdensome', 'burdensomely', 'burn', 'burned', 'burning', 'burns', 'bust', 'busts', 'busybody', 'butcher', 'butchery', 'buzzing', 'byzantine', 'cackle', 'cajole', 'calamities', 'calamitous', 'calamitously', 'calamity', 'callous', 'calm', 'calming', 'calmness', 'calumniate', 'calumniation', 'calumnies', 'calumnious', 'calumniously', 'calumny', 'cancer', 'cancerous', 'cannibal', 'cannibalize', 'capability', 'capable', 'capably', 'capitulate', 'capricious', 'capriciously', 'capriciousness', 'capsize', 'captivate', 'captivating', 'carefree', 'careless', 'carelessness', 'caricature', 'carnage', 'carp', 'cartoonish', 'cash-strapped', 'cashback', 'cashbacks', 'castigate', 'castrated', 'casualty', 'cataclysm', 'cataclysmal', 'cataclysmic', 'cataclysmically', 'catastrophe', 'catastrophes', 'catastrophic', 'catastrophically', 'catastrophies', 'catchy', 'caustic', 'caustically', 'cautionary', 'cave', 'celebrate', 'celebrated', 'celebration', 'celebratory', 'censure', 'chafe', 'chaff', 'chagrin', 'challenging', 'champ', 'champion', 'chaos', 'chaotic', 'charisma', 'charismatic', 'charitable', 'charm', 'charming', 'charmingly', 'chaste', 'chasten', 'chastise', 'chastisement', 'chatter', 'chatterbox', 'cheap', 'cheapen', 'cheaper', 'cheapest', 'cheaply', 'cheat', 'cheated', 'cheater', 'cheating', 'cheats', 'checkered', 'cheer', 'cheerful', 'cheerless', 'cheery', 'cheesy', 'cherish', 'cherished', 'cherub', 'chic', 'chide', 'childish', 'chill', 'chilly', 'chintzy', 'chivalrous', 'chivalry', 'choke', 'choleric', 'choppy', 'chore', 'chronic', 'chunky', 'civility', 'civilize', 'clamor', 'clamorous', 'clarity', 'clash', 'classic', 'classy', 'clean', 'cleaner', 'cleanest', 'cleanliness', 'cleanly', 'clear', 'clear-cut', 'cleared', 'clearer', 'clearly', 'clears', 'clever', 'cleverly', 'cliche', 'cliched', 'clique', 'clog', 'clogged', 'clogs', 'cloud', 'clouding', 'cloudy', 'clueless', 'clumsy', 'clunky', 'coarse', 'cocky', 'coerce', 'coercion', 'coercive', 'cohere', 'coherence', 'coherent', 'cohesive', 'cold', 'coldly', 'collapse', 'collude', 'collusion', 'colorful', 'combative', 'combust', 'comely', 'comfort', 'comfortable', 'comfortably', 'comforting', 'comfy', 'comical', 'commend', 'commendable', 'commendably', 'commiserate', 'commitment', 'commodious', 'commonplace', 'commotion', 'commotions', 'compact', 'compactly', 'compassion', 'compassionate', 'compatible', 'competitive', 'complacent', 'complain', 'complained', 'complaining', 'complains', 'complaint', 'complaints', 'complement', 'complementary', 'complemented', 'complements', 'complex', 'compliant', 'complicated', 'complication', 'complicit', 'compliment', 'complimentary', 'comprehensive', 'compulsion', 'compulsive', 'concede', 'conceded', 'conceit', 'conceited', 'concen', 'concens', 'concern', 'concerned', 'concerns', 'concession', 'concessions', 'conciliate', 'conciliatory', 'concise', 'condemn', 'condemnable', 'condemnation', 'condemned', 'condemns', 'condescend', 'condescending', 'condescendingly', 'condescension', 'confess', 'confession', 'confessions', 'confidence', 'confident', 'confined', 'conflict', 'conflicted', 'conflicting', 'conflicts', 'confound', 'confounded', 'confounding', 'confront', 'confrontation', 'confrontational', 'confuse', 'confused', 'confuses', 'confusing', 'confusion', 'confusions', 'congenial', 'congested', 'congestion', 'congratulate', 'congratulation', 'congratulations', 'congratulatory', 'cons', 'conscientious', 'conscons', 'conservative', 'considerate', 'consistent', 'consistently', 'conspicuous', 'conspicuously', 'conspiracies', 'conspiracy', 'conspirator', 'conspiratorial', 'conspire', 'consternation', 'constructive', 'consummate', 'contagious', 'contaminate', 'contaminated', 'contaminates', 'contaminating', 'contamination', 'contempt', 'contemptible', 'contemptuous', 'contemptuously', 'contend', 'contention', 'contentious', 'contentment', 'continuity', 'contort', 'contortions', 'contradict', 'contradiction', 'contradictory', 'contrariness', 'contrasty', 'contravene', 'contribution', 'contrive', 'contrived', 'controversial', 'controversy', 'convenience', 'convenient', 'conveniently', 'convience', 'convienient', 'convient', 'convincing', 'convincingly', 'convoluted', 'cool', 'coolest', 'cooperative', 'cooperatively', 'cornerstone', 'correct', 'correctly', 'corrode', 'corrosion', 'corrosions', 'corrosive', 'corrupt', 'corrupted', 'corrupting', 'corruption', 'corrupts', 'corruptted', 'cost-effective', 'cost-saving', 'costlier', 'costly', 'counter-attack', 'counter-attacks', 'counter-productive', 'counterproductive', 'coupists', 'courage', 'courageous', 'courageously', 'courageousness', 'courteous', 'courtly', 'covenant', 'covetous', 'coward', 'cowardly', 'cozy', 'crabby', 'crack', 'cracked', 'cracks', 'craftily', 'craftly', 'crafty', 'cramp', 'cramped', 'cramping', 'cranky', 'crap', 'crappy', 'craps', 'crash', 'crashed', 'crashes', 'crashing', 'crass', 'craven', 'cravenly', 'craze', 'crazily', 'craziness', 'crazy', 'creak', 'creaking', 'creaks', 'creative', 'credence', 'credible', 'credulous', 'creep', 'creeping', 'creeps', 'creepy', 'crept', 'crime', 'criminal', 'cringe', 'cringed', 'cringes', 'cripple', 'crippled', 'cripples', 'crippling', 'crisis', 'crisp', 'crisper', 'critic', 'critical', 'criticism', 'criticisms', 'criticize', 'criticized', 'criticizing', 'critics', 'cronyism', 'crook', 'crooked', 'crooks', 'crowded', 'crowdedness', 'crude', 'cruel', 'crueler', 'cruelest', 'cruelly', 'cruelness', 'cruelties', 'cruelty', 'crumble', 'crumbling', 'crummy', 'crumple', 'crumpled', 'crumples', 'crush', 'crushed', 'crushing', 'cry', 'culpable', 'culprit', 'cumbersome', 'cunt', 'cunts', 'cuplrit', 'cure', 'cure-all', 'curse', 'cursed', 'curses', 'curt', 'cushy', 'cuss', 'cussed', 'cute', 'cuteness', 'cutthroat', 'cynical', 'cynicism', 'd*mn', 'damage', 'damaged', 'damages', 'damaging', 'damn', 'damnable', 'damnably', 'damnation', 'damned', 'damning', 'damper', 'danger', 'dangerous', 'dangerousness', 'danke', 'danken', 'daring', 'daringly', 'dark', 'darken', 'darkened', 'darker', 'darkness', 'darling', 'dashing', 'dastard', 'dastardly', 'daunt', 'daunting', 'dauntingly', 'dauntless', 'dawdle', 'dawn', 'daze', 'dazed', 'dazzle', 'dazzled', 'dazzling', 'dead', 'dead-cheap', 'dead-on', 'deadbeat', 'deadlock', 'deadly', 'deadweight', 'deaf', 'dearth', 'death', 'debacle', 'debase', 'debasement', 'debaser', 'debatable', 'debauch', 'debaucher', 'debauchery', 'debilitate', 'debilitating', 'debility', 'debt', 'debts', 'decadence', 'decadent', 'decay', 'decayed', 'deceit', 'deceitful', 'deceitfully', 'deceitfulness', 'deceive', 'deceiver', 'deceivers', 'deceiving', 'decency', 'decent', 'deception', 'deceptive', 'deceptively', 'decisive', 'decisiveness', 'declaim', 'decline', 'declines', 'declining', 'decrement', 'decrepit', 'decrepitude', 'decry', 'dedicated', 'defamation', 'defamations', 'defamatory', 'defame', 'defeat', 'defeated', 'defeating', 'defeats', 'defect', 'defective', 'defects', 'defender', 'defensive', 'deference', 'defiance', 'defiant', 'defiantly', 'deficiencies', 'deficiency', 'deficient', 'defile', 'defiler', 'deform', 'deformed', 'defrauding', 'deft', 'defunct', 'defy', 'degenerate', 'degenerately', 'degeneration', 'deginified', 'degradation', 'degrade', 'degrading', 'degradingly', 'dehumanization', 'dehumanize', 'deign', 'deject', 'dejected', 'dejectedly', 'dejection', 'delay', 'delayed', 'delaying', 'delays', 'delectable', 'delicacy', 'delicate', 'delicious', 'delight', 'delighted', 'delightful', 'delightfully', 'delightfulness', 'delinquency', 'delinquent', 'delirious', 'delirium', 'delude', 'deluded', 'deluge', 'delusion', 'delusional', 'delusions', 'demean', 'demeaning', 'demise', 'demolish', 'demolisher', 'demon', 'demonic', 'demonize', 'demonized', 'demonizes', 'demonizing', 'demoralize', 'demoralizing', 'demoralizingly', 'denial', 'denied', 'denies', 'denigrate', 'denounce', 'dense', 'dent', 'dented', 'dents', 'denunciate', 'denunciation', 'denunciations', 'deny', 'denying', 'dependable', 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'unhealthy', 'unhelpful', 'unilateralism', 'unimaginable', 'unimaginably', 'unimportant', 'uninformed', 'uninsured', 'unintelligible', 'unintelligile', 'unipolar', 'unity', 'unjust', 'unjustifiable', 'unjustifiably', 'unjustified', 'unjustly', 'unkind', 'unkindly', 'unknown', 'unlamentable', 'unlamentably', 'unlawful', 'unlawfully', 'unlawfulness', 'unleash', 'unlicensed', 'unlikely', 'unlimited', 'unlucky', 'unmatched', 'unmoved', 'unnatural', 'unnaturally', 'unnecessary', 'unneeded', 'unnerve', 'unnerved', 'unnerving', 'unnervingly', 'unnoticed', 'unobserved', 'unorthodox', 'unorthodoxy', 'unparalleled', 'unpleasant', 'unpleasantries', 'unpopular', 'unpredictable', 'unprepared', 'unproductive', 'unprofessional', 'unprofitable', 'unprove', 'unproved', 'unproven', 'unproves', 'unproving', 'unqualified', 'unquestionable', 'unquestionably', 'unravel', 'unraveled', 'unreachable', 'unreadable', 'unreal', 'unrealistic', 'unreasonable', 'unreasonably', 'unrelenting', 'unrelentingly', 'unreliability', 'unreliable', 'unresolved', 'unresponsive', 'unrest', 'unrestricted', 'unrivaled', 'unruly', 'unsafe', 'unsatisfactory', 'unsavory', 'unscrupulous', 'unscrupulously', 'unsecure', 'unseemly', 'unselfish', 'unsettle', 'unsettled', 'unsettling', 'unsettlingly', 'unskilled', 'unsophisticated', 'unsound', 'unspeakable', 'unspeakablely', 'unspecified', 'unstable', 'unsteadily', 'unsteadiness', 'unsteady', 'unsuccessful', 'unsuccessfully', 'unsupported', 'unsupportive', 'unsure', 'unsuspecting', 'unsustainable', 'untenable', 'untested', 'unthinkable', 'unthinkably', 'untimely', 'untouched', 'untrue', 'untrustworthy', 'untruthful', 'unusable', 'unusably', 'unuseable', 'unuseably', 'unusual', 'unusually', 'unviewable', 'unwanted', 'unwarranted', 'unwatchable', 'unwavering', 'unwelcome', 'unwell', 'unwieldy', 'unwilling', 'unwillingly', 'unwillingness', 'unwise', 'unwisely', 'unworkable', 'unworthy', 'unyielding', 'upbeat', 'upbraid', 'upgradable', 'upgradeable', 'upgraded', 'upheaval', 'upheld', 'uphold', 'uplift', 'uplifting', 'upliftingly', 'upliftment', 'uprising', 'uproar', 'uproarious', 'uproariously', 'uproarous', 'uproarously', 'uproot', 'upscale', 'upset', 'upseting', 'upsets', 'upsetting', 'upsettingly', 'urgent', 'usable', 'useable', 'useful', 'useless', 'user-friendly', 'user-replaceable', 'usurp', 'usurper', 'utterly', 'vagrant', 'vague', 'vagueness', 'vain', 'vainly', 'valiant', 'valiantly', 'valor', 'valuable', 'vanity', 'variety', 'vehement', 'vehemently', 'venerate', 'vengeance', 'vengeful', 'vengefully', 'vengefulness', 'venom', 'venomous', 'venomously', 'vent', 'verifiable', 'veritable', 'versatile', 'versatility', 'vestiges', 'vex', 'vexation', 'vexing', 'vexingly', 'vibrant', 'vibrantly', 'vibrate', 'vibrated', 'vibrates', 'vibrating', 'vibration', 'vice', 'vicious', 'viciously', 'viciousness', 'victimize', 'victorious', 'victory', 'viewable', 'vigilance', 'vigilant', 'vile', 'vileness', 'vilify', 'villainous', 'villainously', 'villains', 'villian', 'villianous', 'villianously', 'villify', 'vindictive', 'vindictively', 'vindictiveness', 'violate', 'violation', 'violator', 'violators', 'violent', 'violently', 'viper', 'virtue', 'virtuous', 'virtuously', 'virulence', 'virulent', 'virulently', 'virus', 'visionary', 'vivacious', 'vivid', 'vociferous', 'vociferously', 'volatile', 'volatility', 'vomit', 'vomited', 'vomiting', 'vomits', 'vouch', 'vouchsafe', 'vulgar', 'vulnerable', 'wack', 'wail', 'wallow', 'wane', 'waning', 'wanton', 'war-like', 'warily', 'wariness', 'warlike', 'warm', 'warmer', 'warmhearted', 'warmly', 'warmth', 'warned', 'warning', 'warp', 'warped', 'wary', 'washed-out', 'waste', 'wasted', 'wasteful', 'wastefulness', 'wasting', 'water-down', 'watered-down', 'wayward', 'weak', 'weaken', 'weakening', 'weaker', 'weakness', 'weaknesses', 'wealthy', 'weariness', 'wearisome', 'weary', 'wedge', 'weed', 'weep', 'weird', 'weirdly', 'welcome', 'well', 'well-backlit', 'well-balanced', 'well-behaved', 'well-being', 'well-bred', 'well-connected', 'well-educated', 'well-established', 'well-informed', 'well-intentioned', 'well-known', 'well-made', 'well-managed', 'well-mannered', 'well-positioned', 'well-received', 'well-regarded', 'well-rounded', 'well-run', 'well-wishers', 'wellbeing', 'wheedle', 'whimper', 'whine', 'whining', 'whiny', 'whips', 'whoa', 'wholeheartedly', 'wholesome', 'whooa', 'whoooa', 'whore', 'whores', 'wicked', 'wickedly', 'wickedness', 'wieldy', 'wild', 'wildly', 'wiles', 'willing', 'willingly', 'willingness', 'wilt', 'wily', 'wimpy', 'win', 'wince', 'windfall', 'winnable', 'winner', 'winners', 'winning', 'wins', 'wisdom', 'wise', 'wisely', 'witty', 'wobble', 'wobbled', 'wobbles', 'woe', 'woebegone', 'woeful', 'woefully', 'womanizer', 'womanizing', 'won', 'wonder', 'wonderful', 'wonderfully', 'wonderous', 'wonderously', 'wonders', 'wondrous', 'woo', 'work', 'workable', 'worked', 'works', 'world-famous', 'worn', 'worried', 'worriedly', 'worrier', 'worries', 'worrisome', 'worry', 'worrying', 'worryingly', 'worse', 'worsen', 'worsening', 'worst', 'worth', 'worth-while', 'worthiness', 'worthless', 'worthlessly', 'worthlessness', 'worthwhile', 'worthy', 'wound', 'wounds', 'wow', 'wowed', 'wowing', 'wows', 'wrangle', 'wrath', 'wreak', 'wreaked', 'wreaks', 'wreck', 'wrest', 'wrestle', 'wretch', 'wretched', 'wretchedly', 'wretchedness', 'wrinkle', 'wrinkled', 'wrinkles', 'wrip', 'wripped', 'wripping', 'writhe', 'wrong', 'wrongful', 'wrongly', 'wrought', 'yawn', 'yay', 'youthful', 'zap', 'zapped', 'zaps', 'zeal', 'zealot', 'zealous', 'zealously', 'zenith', 'zest', 'zippy', 'zombie', 'FALSE']
  `GENERIC_QUANTIFIERS_LEX` = ['whole', 'vast', 'various', 'varied', 'several', 'numerous', 'multiple', 'small', 'certain', 'many', 'more', 'much', 'many', 'few', 'enough', 'most', 'plenty', 'sole', 'serial', 'such', 'less', 'further', 'additional', 'entire', 'whole']
  `GEOGRAPHICAL_ADJECTIVES_LEX` = ['global', 'universal', 'regional', 'rural', 'globalscale', 'indoor', 'inside', 'international', 'local', 'localized', 'multinational', 'near', 'nearfuture', 'nearby', 'central', 'centric', 'bottom', 'centralized', 'left', 'located', 'middle', 'national', 'demographic', 'adjacent', 'front', 'urban', 'southern', 'northern']
  `INTENSIFIERS_LEX` = ['very', 'super', 'bit', 'extremely', 'highly', 'little', 'most', 'slightly', 'somewhat', 'absolutely', 'completely', 'reasonably', 'beyond', 'huge', 'big']
  `NEGATION_LEX` = ['barely', 'hardly', 'lack', 'least', 'neither', 'never', 'nor', 'not', "n't", 'without', 'non']
  `NUM_OF_SENTENCES_PER_OPINION_AND_ASPECT_TERM_INC` = 35000
  `OPINION_STOP_LIST` = <nlp_architect.models.absa.train.data_types.LoadOpinionStopLists object>
  `ORDINAL_NUMBERS_LEX` = ['second', 'third', 'fourth', 'fifth', 'seventh', 'eighth', 'ninth', 'tenth', 'eleventh', 'twelfth', 'thirteenth', 'fourteenth', 'fifteenth', 'sixteenth', 'seventeenth', 'eighteenth', 'nineteenth', 'twentieth', 'twenty-first', 'twenty-second', 'twenty-third', 'twenty-fourth', 'twenty-fifth', 'twenty-sixth', 'twenty-seventh', 'twenty-eighth', 'twenty-ninth', 'thirtieth', 'thirty-first', 'fortieth', 'fiftieth', 'sixtieth', 'seventieth', 'ninetieth', 'million']
  `PREPOSITIONS_LEX` = ['above', 'after', 'against', 'among', 'amongst', 'at', 'backagainst', 'below', 'beneath', 'beyond', 'by', 'during', 'following', 'for', 'from', 'in', 'near', 'nearby', 'of', 'on', 'over', 'through', 'to', 'under', 'until', 'with', 'within', 'without']
  `PRONOUNS_LEX` = ['i', 'me', 'we', 'us', 'you', 'she', 'her', 'he', 'him', 'they', 'them', 'it', 'my', 'mine', 'your', 'yours', 'his', 'her', 'its', 'our', 'ours', 'your', 'yours', 'their', 'theirs']
  `TIME_ADJECTIVE_LEX` = ['everyday', 'daily', 'recent', 'future', 'sixmonth', 'todays', 'twoday', 'annual', 'upcoming', 'weeklong', 'weekly', 'yearlong', 'yearly', 'yeararound', 'yeartodate', 'yesterdays', 'zeroday', 'early', 'continuous', 'current', 'daytoday', 'fullday', 'fulltime', 'fullyear', 'futuristic', 'next', 'mundane', 'present', 'prior', 'forthcoming', 'foreseeable', 'fiveyear', 'consecutive', 'long term', 'late', 'later', 'monthly', 'previous']
  `acquire_lexicons`(parsed_dir: str)

    Acquire new opinion and aspect lexicons.

    Parameters: | parsed_dir (PathLike) – Path to parsed documents folder.

  `acquired_aspect_terms_path` = PosixPath('/Users/pizsak/nlp-architect/cache/absa/train/lexicons/generated_aspect_lex.csv')
  `acquired_opinion_terms_path` = PosixPath('/Users/pizsak/nlp-architect/cache/absa/train/lexicons/generated_opinion_lex.csv')
  `extract_opinion_and_aspect_terms`(parsed_document_iter, num_of_docs)

    Extract candidate terms from parsed document iterator.

    Parameters: | parsed_document_iter (Iterator) – Parsed document iterator. num_of_docs (int) – number of documents on iterator.

  `extract_terms_from_doc`(parsed_doc)

    Extract candidate terms for sentences in parsed document.

    Parameters: | parsed_doc (ParsedDocument) – Input parsed document.

  `generic_opinion_lex_path` = PosixPath('/Users/pizsak/Peter/Repositories/nlp-architect/nlp_architect/models/absa/train/lexicons/GenericOpinionLex.csv')

`nlp_architect.models.absa.train.acquire_terms.``generate_final_opinion_candidates_list`(opinion_candidate_list_raw, final_opinion_candidates_list, frequency_threshold)

  generate final opinion candidates list from raw opinion candidate list :param opinion_candidate_list_raw: key = term, value = :type opinion_candidate_list_raw: dict :param lists of extracted terms.: :param final_opinion_candidates_list: list of final opinion candidates :type final_opinion_candidates_list: list :param frequency_threshold: minimum freq. for qualifying term :type frequency_threshold: int

## nlp_architect.models.absa.train.data_types module

class `nlp_architect.models.absa.train.data_types.``AspectTerm`(terms, pos, lemmas)

  Bases: `object`

  Aspect term.

  `terms`

    list of terms

    Type: | list

  `pos`

    list of pos

    Type: | list

  static `from_token`(token)

class `nlp_architect.models.absa.train.data_types.``CandidateTerm`(term_a, term_b, sent_text, candidate_term_polarity)

  Bases: `object`

  Candidate opinion term or aspect term.

  `term`

    list of terms

    Type: | list

  `pos`

    list of pos

    Type: | list

  `source_term`

    list of related anchor terms

    Type: | list

  `sentence`

    sentence text this term

    Type: | str

  `term_polarity`

    term polarity

    Type: | int

class `nlp_architect.models.absa.train.data_types.``DepRelation`(gov=None, dep=None, rel=None)

  Bases: `object`

  Generic Relation Entry contains the governor, it’s dependent and the relation between them.

  `gov`

    governor

    Type: | DepRelationTerm

  `dep`

    dependent

    Type: | DepRelationTerm

  `rel`

    relation type between governor and dependent

    Type: | str

class `nlp_architect.models.absa.train.data_types.``DepRelationTerm`(text=None, lemma=None, pos=None, ner=None, idx=None)

  Bases: `object`

  `text`

    token text

    Type: | str, optional

  `lemma`

    token lemma

    Type: | str, optional

  `pos`

    token pos

    Type: | str, optional

  `ner`

    token ner

    Type: | str, optional

  `idx`

    token start index (within the sentence)

    Type: | int, optional

  `norm_pos`

class `nlp_architect.models.absa.train.data_types.``LoadAspectStopLists`(generic_opinion_lex, determiners_lex, general_adjectives_lex, generic_quantifiers_lex, geographical_adjectives_lex, intensifiers_lex, time_adjective_lex, ordinal_numbers_lex, prepositions_lex, pronouns_lex, colors_lex, negation_lex, auxiliaries_lex)

  Bases: `object`

  A Filter holding all generic and general lexicons, can verify if a given term is contained
    in one of the lexicons - hence belongs to one of the generic / general lexicons or is a valid term.
  `generic_opinion_lex`

    generic opinion lexicon

    Type: | dict

  `determiners_lex`

    determiners lexicon

    Type: | dict

  `general_adjectives_lex`

    general adjectives lexicon

    Type: | dict

  `generic_quantifiers_lex`

    generic quantifiers lexicon

    Type: | dict

  `geographical_adjectives_lex`

    geographical adjectives lexicon

    Type: | dict

  `intensifiers_lex`

    intensifiers lexicon

    Type: | dict

  `time_adjective_lex`

    time adjective lexicon

    Type: | dict

  `ordinal_numbers_lex`

    ordinal numbers lexicon

    Type: | dict

  `prepositions_lex`

    prepositions lexicon

    Type: | dict

  `pronouns_lex`

    pronouns lexicon

    Type: | dict

  `colors_lex`

    colors lexicon

    Type: | dict

  `negation_lex`

    negation terms lexicon

    Type: | dict

  `is_in_stop_list`(term)

class `nlp_architect.models.absa.train.data_types.``LoadOpinionStopLists`(determiners_lex, general_adjectives_lex, generic_quantifiers_lex, geographical_adjectives_lex, intensifiers_lex, time_adjective_lex, ordinal_numbers_lex, prepositions_lex, colors_lex, negation_lex)

  Bases: `object`

  A Filter holding all generic and general lexicons, can verify if a given term is contained
    in one of the lexicons - hence belongs to one of the generic / general lexicons or is a valid term.
  `determiners_lex`

    determiners lexicon

    Type: | dict

  `general_adjectives_lex`

    general adjectives lexicon

    Type: | dict

  `generic_quantifiers_lex`

    generic quantifiers lexicon

    Type: | dict

  `geographical_adjectives_lex`

    geographical adjectives lexicon

    Type: | dict

  `intensifiers_lex`

    intensifiers lexicon

    Type: | dict

  `time_adjective_lex`

    time adjective lexicon

    Type: | dict

  `ordinal_numbers_lex`

    ordinal numbers lexicon

    Type: | dict

  `prepositions_lex`

    prepositions lexicon

    Type: | dict

  `colors_lex`

    colors lexicon

    Type: | dict

  `negation_lex`

    negation terms lexicon

    Type: | dict

  `is_in_stop_list`(term)

class `nlp_architect.models.absa.train.data_types.``OpinionTerm`(terms, polarity)

  Bases: `object`

  Opinion term.

  `terms`

    list of opinion term polarity (Polarity): polarity of the sentiment

    Type: | list

class `nlp_architect.models.absa.train.data_types.``POS`

  Bases: `enum.Enum`

  Part-of-speech labels.

  `ADJ` = 1
  `ADV` = 2
  `AUX` = 3
  `AUX_PAST` = 3
  `CONJ` = 4
  `DET` = 6
  `EX` = 7
  `FW` = 8
  `IN` = 9
  `INTERJ` = 35
  `LS` = 11
  `MD` = 12
  `MD_CERTAIN` = 13
  `NN` = 14
  `NUM` = 5
  `OTHER` = 45
  `POS` = 16
  `POSSPRON_1_P` = 25
  `POSSPRON_1_S` = 24
  `POSSPRON_2_P` = 27
  `POSSPRON_2_S` = 26
  `POSSPRON_3_P` = 29
  `POSSPRON_3_S` = 28
  `POSSPRON_4_P` = 31
  `POSSPRON_4_S` = 30
  `PREP` = 10
  `PRON` = 17
  `PRON_1_P` = 19
  `PRON_1_S` = 18
  `PRON_2_S` = 20
  `PRON_3_P` = 22
  `PRON_3_S` = 21
  `PRON_4_S` = 23
  `PROPER_NAME` = 15
  `PUNCT` = 44
  `RP` = 32
  `SYM` = 33
  `TO` = 34
  `VB` = 36
  `VBG` = 39
  `VBN` = 40
  `VB_PAST` = 37
  `VB_PRESENT` = 38
  `WH_ADV` = 43
  `WH_DET` = 41
  `WH_PROP` = 42

class `nlp_architect.models.absa.train.data_types.``QualifiedTerm`(term, lemma, pos, frequency, term_polarity)

  Bases: `object`

  Qualified term - term that is accepted to generated lexicon.

  `term`

    list of terms

    Type: | list

  `pos`

    list of pos.

    Type: | list

  `frequency`

    frequency of filtered term in corpus.

    Type: | int

  `term_polarity`

    term polarity.

    Type: | Polarity

  `as_string_list`()
  `as_string_list_aspect`()
  `as_string_list_aspect_debug`()

class `nlp_architect.models.absa.train.data_types.``RelCategory`

  Bases: `enum.Enum`

  An enumeration.

  `MOD` = {'acl', 'advcl', 'amod', 'appos', 'neg', 'nmod'}
  `OBJ` = {'dobj', 'iobj'}
  `SUBJ` = {'csubj', 'csubjpass', 'nsubj', 'nsubjpass'}

`nlp_architect.models.absa.train.data_types.``load_lex_as_dict_from_csv`(file_name: str)

  Read lexicon as dictionary, key = term, value = pos.

  Parameters: | file_name – the csv file name

`nlp_architect.models.absa.train.data_types.``normalize_pos`(word, in_pos)

## nlp_architect.models.absa.train.generate_lexicons module

`nlp_architect.models.absa.train.generate_lexicons.``generate_final_aspect_candidates_list`(aspect_candidate_list_raw, final_aspect_candidates_list, frequency_threshold)

  Generate final aspect candidates list from map.

  Parameters: | aspect_candidate_list_raw (dict) – key = term, value = of candidate terms. (lists) – final_aspect_candidates_list (list) – list of final aspect candidates frequency_threshold (int) – minimum freq. for qualifying term

`nlp_architect.models.absa.train.generate_lexicons.``generate_final_opinion_candidates_list`(opinion_candidate_list_raw, final_opinion_candidates_list, frequency_threshold)

  Generate final opinion candidates list from raw opinion candidate list.

  Parameters: | opinion_candidate_list_raw (dict) – key = term, value = of extracted terms. (lists) – final_opinion_candidates_list (list) – list of final opinion candidates frequency_threshold (int) – minimum freq. for qualifying term

`nlp_architect.models.absa.train.generate_lexicons.``set_opinion_term_polarity`(terms_list)

  Set opinion term polarity.

  Parameters: | terms_list (list) – list of opinion terms

## nlp_architect.models.absa.train.rerank_terms module

class `nlp_architect.models.absa.train.rerank_terms.``RerankTerms`(vector_cache=True, rerank_model: os.PathLike = None, emb_model_path: os.PathLike = None)

  Bases: `object`

  `PREDICTION_THRESHOLD` = 0.7
  `calc_cosine_similarity`(word_1, word_2, embedding_dict)

    calculate cosine similarity scores between 2 terms

    Parameters: | word_1 (str) – 1st input word word_2 (str) – 2nd input word embedding_dict (dict) – embedding dictionary
    Returns: | similarity scores between the 2 input words
    Return type: | vectors_sim_dict[key] (float)

  `calc_similarity_scores_for_all_terms`(terms, generic_terms, embedding_dict)

    calculate similarity scores between each term and each off the generic terms

    Parameters: | terms – candidate terms generic_terms – generic opinion terms embedding_dict – embedding dictionary
    Returns: | similarity scores between each cand term and neg generic term pos_all: similarity scores between each cand term and pos generic term
    Return type: | neg_all

  static `concat_sim_scores_and_features`(x, neg_sim, pos_sim)

    concatenate similarity scores to features

    Parameters: | x – feature vector neg_sim – similarity scores between cand terms and neg opinion terms pos_sim – similarity scores between cand terms and pos opinion terms
    Returns: | concatenated features and similarity scores
    Return type: | x

  `cross_validation_training`(verbose=False)

    Perform k fold cross validation and evaluate the results.

  `evaluate`(model, x_test, y_test, terms)
  `generate_embbeding_features`(terms, embedding_dict)

    concatenate word embedding to features

    Parameters: | terms – candidate terms embedding_dict – embedding dictionary word_to_emb_idx – index to embedding dictionary
    Returns: | concatenated features and word embs
    Return type: | x

  `generate_features`(terms, polarities)
  `generate_model`(input_vector_dimension)

    Generate MLP model.

    Parameters: | input_vector_dimension (int) – word emb vec length

    Returns:

  `load_terms_and_generate_features`(filename)

    load candidate terms with their basic features, Y labels and polarities from feature file

    Parameters: | filename – feature table file path
    Returns: | feature vector terms: candidate terms polarities: opinion polarity per term
    Return type: | x

  static `load_terms_and_polarities`(filename)

    load terms and polarities from file

    Parameters: | filename – feature table file full path
    Returns: | candidate terms polarities: opinion polarity per term
    Return type: | terms

  static `load_terms_and_y_labels`(filename)

    Load terms and Y labels from feature file.

    Parameters: | filename – feature table file full path
    Returns: | feature vector y: labels vector terms: candidate terms polarities: opinion polarity per term
    Return type: | x

  `load_terms_and_y_labels_and_generate_features`(filename)

    load candidate terms with their basic features, Y labels and polarities from feature file

    Parameters: | filename – feature table file path
    Returns: | feature vector y: labels vector terms: candidate terms polarities: opinion polarity per term
    Return type: | x

  static `load_word_vectors_dict`()
  `model_dir` = PosixPath('/Users/pizsak/nlp-architect/cache/absa/train/reranking_model')
  `predict`(input_table_file, generic_opinion_terms)

    Predict classification class according to model.

    Parameters: | input_table_file – feature(X) and labels(Y) table file generic_opinion_terms – generic opinion terms file name
    Returns: | reranked_lex conctenated with generic lex
    Return type: | final_concat_opinion_lex

  `print_evaluation_results`(precision, recall, f1)

    Print evaluation results.

    Parameters: | precision (list of float) – precision recall (list of float) – recall f1 (list of float) – f measure

  static `print_measure`(measure, value)

    Print single measure.

    Parameters: | measure (str) – measure type value (list of float) – value

  `print_params`(batch_size, epochs, seed)

    Print training params.

    Parameters: | batch_size (int) – batch size epochs (int) – num of epochs seed (int) – seed

  `rerank_train`()

    Class for training a reranking model.

  `train_rerank_data_path` = PosixPath('/Users/pizsak/Peter/Repositories/nlp-architect/nlp_architect/models/absa/train/lexicons/RerankTrainingData.csv')
  static `write_evaluation_report`(report_dic)

## nlp_architect.models.absa.train.rules module

`nlp_architect.models.absa.train.rules.``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.

`nlp_architect.models.absa.train.rules.``is_subj_obj_or_mod`(rt)

`nlp_architect.models.absa.train.rules.``rule_1`(dep_rel, gov_entry, dep_entry, text)

  Extract term if rule 1 applies.

  Parameters: | dep_rel (DepRelation) – Dependency relation. gov_entry (DicEntrySentiment) – Governor opinion entry. dep_entry (DicEntrySentiment) – Dependant opinion entry. text (str) – Sentence text.

`nlp_architect.models.absa.train.rules.``rule_2`(dep_rel, relation_list, dep_entry, text)

  Extract term if rule 2 applies.

  Parameters: | dep_rel (DepRelation) – Dependency relation. relation_list (list of DepRelation) – Generic relations between all tokens. dep_entry (OpinionTerm) – Dependent token. text (str) – Sentence text.

`nlp_architect.models.absa.train.rules.``rule_3`(dep_rel, relation_list, text)

  Extract term if rule 3 applies.

  Parameters: | dep_rel (DepRelation) – Dependency relation. relation_list (list of DepRelation) – Generic relations between all tokens. text (str) – Sentence text.

`nlp_architect.models.absa.train.rules.``rule_4`(dep_rel, relation_list, text)

  Extract term if rule 4 applies.

  Parameters: | dep_rel (DepRelation) – Dependency relation. relation_list (list of DepRelation) – Generic relations between all tokens. between tokens (relation) – text (str) – Sentence text.

`nlp_architect.models.absa.train.rules.``rule_5`(dep_rel, text)

  Extract term if rule 5 applies.

  Parameters: | dep_rel (DepRelation) – Dependency relation. text (str) – Sentence text.

`nlp_architect.models.absa.train.rules.``rule_6`(dep_rel, relation_list, text)

  Extract term if rule 6 applies.

  Parameters: | dep_rel (DepRelation) – Dependency relation. relation_list (list of DepRelation) – Generic relations between all tokens. text (str) – Sentence text.

## nlp_architect.models.absa.train.train module

class `nlp_architect.models.absa.train.train.``TrainSentiment`(parse: bool = True, rerank_model: os.PathLike = None, asp_thresh: int = 3, op_thresh: int = 2, max_iter: int = 3)

  Bases: `object`

  `parse_data`(data: os.PathLike, parsed_dir: os.PathLike)
  `run`(data: Union[str, os.PathLike] = None, parsed_data: Union[str, os.PathLike] = None, out_dir: Union[str, os.PathLike] = PosixPath('/Users/pizsak/nlp-architect/cache/absa/train'))

## Module contents