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generation
semeval-2014
[ "There was a great deal for 6 Blue Point oysters and a beer or glass of wine for $8!" ]
{'aspect_term': [['Blue Point oysters', 'neutral'], ['beer', 'neutral'], ['glass of wine', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I could have drank 4 glasses of water and still been parched - so watch out." ]
{'aspect_term': [['glasses of water', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Won't or Can't is not in the service directory." ]
{'aspect_term': [['service', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "We go on Mondays for the prix fixe and our experience with the food has been comparable to Blue Ribbon." ]
{'aspect_term': [['food', 'neutral'], ['prix fixe', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food is mostly made from scratch, fresh and well prepared." ]
{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "But who says Murray's is anything about service." ]
{'aspect_term': [['service', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I found the food, service and value exceptional everytime I have been there." ]
{'aspect_term': [['food', 'positive'], ['service', 'positive'], ['value', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food, drinks and service are clearly among the best in the city." ]
{'aspect_term': [['food', 'positive'], ['drinks', 'positive'], ['service', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Thalia is a beautiful restaurant with beautiful people serving you, but the food doesn't quite match up." ]
{'aspect_term': [['people serving', 'positive'], ['food', 'negative']], 'aspect_category': [[None, 'positive'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The atmosphere is unheralded, the service impecible, and the food magnificant." ]
{'aspect_term': [['atmosphere', 'positive'], ['service', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "i've been back to nha trang literally a hundred times for the beef cubes - they're that good." ]
{'aspect_term': [['beef cubes', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "We were well attended to by the enthusiastic staff especially the manager Tony Gaskin who made excellent suggestions for our menu selections." ]
{'aspect_term': [['staff', 'positive'], ['manager', 'positive'], ['menu selections', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Best Pastrami I ever had and great portion without being ridiculous." ]
{'aspect_term': [['Pastrami', 'positive'], ['portion', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "IT IS WHAT MODERN CUISINE IS ALL ABOUT." ]
{'aspect_term': [['CUISINE', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Each bite of food at Kai was indeed delicious, fresh, and elegant." ]
{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "And Kruno, the beverage manager is the best bartender I have yet to come across." ]
{'aspect_term': [['bartender', 'positive'], ['beverage manager', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food was bland oily." ]
{'aspect_term': [['food', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The tastes makes your mouth water for more." ]
{'aspect_term': [['tastes', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Service was also horrible and the ambience is not that great." ]
{'aspect_term': [['Service', 'negative'], ['ambience', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The barebecued salmon is elegantly spiced and not at all dry." ]
{'aspect_term': [['barebecued salmon', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "now if they could only get a toaster." ]
{'aspect_term': [['toaster', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I had very high expectations for this place and made a reservation a couple of months in advance for a special occasion." ]
{'aspect_term': [['reservation', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Whem asked, we had to ask more detailed questions so that we knew what the specials were." ]
{'aspect_term': [['specials', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Waiters are slow but sweet." ]
{'aspect_term': [['Waiters', 'conflict']], 'aspect_category': [[None, 'conflict']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "While I quite liked the food and the ambience, I'm not quite sure if it they really deserve it the Michelin rating they have displayed so prooudly in the window." ]
{'aspect_term': [['food', 'positive'], ['ambience', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "They've the best desserts and mixed drinks as well as snack foods." ]
{'aspect_term': [['desserts', 'positive'], ['mixed drinks', 'positive'], ['snack foods', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The entire place and the treatment we received felt as a conveyor belt." ]
{'aspect_term': [['place', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Good crowd, good outdoor seating, with a hip japanese vibe." ]
{'aspect_term': [['outdoor seating', 'positive'], ['vibe', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Any if you have a reservation you'll wait for max 5 minutes - so have a drink at the bar." ]
{'aspect_term': [['reservation', 'positive'], ['drink', 'neutral'], ['bar', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Fresh, authentic, french cuisine in substantial portions." ]
{'aspect_term': [['french cuisine', 'positive'], ['portions', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The atmosphere is much better than Sripraphai (more modern and sleek)." ]
{'aspect_term': [['atmosphere', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The banana tower is an amazing dessert as well." ]
{'aspect_term': [['banana tower', 'positive'], ['dessert', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The restaurant looks out over beautiful green lawns to the Hudson River and the Statue of Liberty." ]
{'aspect_term': [['lawns', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "It's really also the service, is good and the waiters are friendly." ]
{'aspect_term': [['service', 'positive'], ['waiters', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The Pad thai, lad nar and various other dishes all look good on paper but, I've had better thai food in less asthetically pleasing places." ]
{'aspect_term': [['Pad thai', 'conflict'], ['lad nar', 'conflict'], ['dishes', 'conflict'], ['places', 'negative'], ['thai food', 'neutral']], 'aspect_category': [[None, 'conflict'], [None, 'conflict'], [None, 'conflict'], [None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The cold appetizer dishes taste like the way I remember them to taste when I was growing up in Taiwan." ]
{'aspect_term': [['cold appetizer dishes', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Restaurant snobs need not bother, this is a small, neighborhood kind of place." ]
{'aspect_term': [['place', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Based on the reviews for dinner, this is a place I would reconsider revisiting for that, but definitely not for Dim Sum again." ]
{'aspect_term': [['Dim Sum', 'negative'], ['dinner', 'positive']], 'aspect_category': [[None, 'negative'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Good, fast service." ]
{'aspect_term': [['service', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The anti-pasta was excellent, especially the calamari, as were the filling pasta mains." ]
{'aspect_term': [['anti-pasta', 'positive'], ['calamari', 'positive'], ['filling pasta mains', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food is a diamond in rough -- the food is delicious and homemade with the perfect balance of herbs and tomatoes." ]
{'aspect_term': [['food', 'positive'], ['food', 'positive'], ['herbs', 'positive'], ['tomatoes', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The jukebox plays everything from Italian Opera to The Strokes." ]
{'aspect_term': [['jukebox', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The bruschetta and panini's are so yummy!" ]
{'aspect_term': [['bruschetta', 'positive'], ['panini', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Great for large groups and celebrations - our SUPER HAPPY waiter was the entertainment of the evening." ]
{'aspect_term': [['waiter', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I don't know about you guys, but when I go somewhere to eat I go for the food not for the atmosphere." ]
{'aspect_term': [['food', 'neutral'], ['atmosphere', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The portions are large and the servers always surprise us with a different starter." ]
{'aspect_term': [['portions', 'positive'], ['servers', 'positive'], ['starter', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Great bagels made the old-fashioned way." ]
{'aspect_term': [['bagels', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The menu consisted of standard brassiere food, better then places like Balthazar etc." ]
{'aspect_term': [['menu', 'positive'], ['brassiere food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This place would be so much better served by being run by a group that actually understands customer service." ]
{'aspect_term': [['service', 'negative'], ['served', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Cornelia Street looks like a Broadway set for West Side Story and the inside of Po is so cool quaint you really can't top the setting for a romantic dinner in NYC." ]
{'aspect_term': [['dinner', 'positive'], ['setting', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "the drinks are amazing and half off till 8pm." ]
{'aspect_term': [['drinks', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Such nice people working here - but I have to review the food." ]
{'aspect_term': [['people', 'positive'], ['food', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Great romantic place for a date (try to get the corner booth table for a little privacy and to sit close!)." ]
{'aspect_term': [['privacy', 'positive'], ['corner booth table', 'positive'], ['place', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The pizza is overpriced and soggy." ]
{'aspect_term': [['pizza', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "We've been to Grocery three times and not once has an item on the menu disappointed." ]
{'aspect_term': [['menu', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The wine list is interesting and has many good values." ]
{'aspect_term': [['wine list', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Overall I was impressed and will return, it's a great QPR (Quality to Price Ratio)." ]
{'aspect_term': [['Price', 'positive'], ['Quality', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This place is worth going even if only for their beer." ]
{'aspect_term': [['beer', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I would recommend Roxy's for that, but not for their food." ]
{'aspect_term': [['food', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Ingredients are organic which is a real plus for me." ]
{'aspect_term': [['Ingredients', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "In fact, two people could really share one plate." ]
{'aspect_term': [['plate', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "If you're looking for a place to sit down, have a drink and conversations with friends, go to sweet-n-tart and order yourself a hong-kong styled milk and tea with tapioca pearls (hot)." ]
{'aspect_term': [['drink', 'neutral'], ['hong-kong styled milk', 'positive'], ['tea with tapioca pearls (hot)', 'positive']], 'aspect_category': [[None, 'neutral'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Delivery is fast too." ]
{'aspect_term': [['Delivery', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "You can also special order any kind of dumpling,etc." ]
{'aspect_term': [['dumpling', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The service was excellent - friendly and attentive." ]
{'aspect_term': [['service', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Should you happen to be impressed by the cuisine definitely try it." ]
{'aspect_term': [['cuisine', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The only thing more wonderful than the food (which is exceptional) is the service." ]
{'aspect_term': [['food', 'positive'], ['service', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I'm partial to the Gnocchi." ]
{'aspect_term': [['Gnocchi', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "So some of the reviews here are accurate about the crowd and noise." ]
{'aspect_term': [['crowd', 'negative'], ['noise', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "One thing I liked about this place is that I never felt rushed or pressured to give up my table ot incoming guests." ]
{'aspect_term': [['table', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Besides having the table we had been promised given to other restaurant patrons twice before we were actually seated, we were served dishes we hadn't ordered three times, received one of our orders 20 minutes after the rest of the table had been served (and that order was undercooked), and charged $45 more than we should have been on our bill." ]
{'aspect_term': [['table', 'negative'], ['served', 'negative'], ['dishes', 'negative'], ['table', 'negative'], ['served', 'negative'], ['bill', 'negative']], 'aspect_category': [[None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "(food was delivered by a busboy, not waiter) We got no cheese offered for the pasta, our water and wine glasses remained EMPTY our entire meal, when we would have easily spent another $20 on wine." ]
{'aspect_term': [['food', 'neutral'], ['busboy', 'negative'], ['waiter', 'negative'], ['cheese', 'negative'], ['pasta', 'negative'], ['water and wine glasses', 'negative'], ['wine', 'neutral'], ['meal', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'negative'], [None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The spicy tuna and salmon are the best we've ever had." ]
{'aspect_term': [['spicy tuna', 'positive'], ['salmon', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Try the Times Square cocktail -- ginger lemonade with vodka (also available without vodka.)" ]
{'aspect_term': [['Times Square cocktail', 'positive'], ['ginger lemonade with vodka', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Good French restaurant in an area devoid of decent restaurants unless you're into eating 4 pound pastrami sandwiches at Katz' Deli, or Mexican food which is supplied by capable restaurants." ]
{'aspect_term': [['pastrami sandwiches', 'neutral'], ['Mexican food', 'neutral']], 'aspect_category': [[None, 'neutral'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "But the staff was so horrible to us." ]
{'aspect_term': [['staff', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "It is nearly impossible to get a table, so if you ever have the chance to go here for dinner, DO NOT pass it up." ]
{'aspect_term': [['table', 'negative'], ['dinner', 'positive']], 'aspect_category': [[None, 'negative'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I went to Del Frisco's Friday night with my boyfriend for an 8:00 reservation." ]
{'aspect_term': [['reservation', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The bagel was huge." ]
{'aspect_term': [['bagel', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The bread is the soft paratha bread (unlike the plain bread they use in Calcutta), and the stuffing is tandoori styled and very flavorful." ]
{'aspect_term': [['bread', 'positive'], ['paratha bread', 'positive'], ['bread', 'negative'], ['stuffing', 'positive'], ['tandoori', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive'], [None, 'negative'], [None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Please try the Filet Mignon, its just the most tender piece ever." ]
{'aspect_term': [['Filet Mignon', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food is above average for midtown and sligtly better than some of the other Heartland Breweries in the city." ]
{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "would have rather tried terrace in the sky or water club for that price" ]
{'aspect_term': [['price', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The staff has always been attentive and kind, and I've always been amazed at how they've handled all the various different group sizes that come in." ]
{'aspect_term': [['staff', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Just because it's cheap does NOT mean the portions are small or the food is nasty, IT IS GREAT!" ]
{'aspect_term': [['portions', 'positive'], ['food', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Meanwhile, the bartender continued to pour champagne from his reserve after we had finished our bottle and we enjoyed an amuse of turnip soup with pureed basil, gratis." ]
{'aspect_term': [['bartender', 'positive'], ['champagne', 'neutral'], ['turnip soup with pureed basil', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'neutral'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The food's as good as ever." ]
{'aspect_term': [['food', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Even with a relatively inexpensive botle of wine, if you can call $70.00 inexpensive, the cost is through the roof for better than average fare." ]
{'aspect_term': [['cost', 'negative'], ['botle of wine', 'conflict']], 'aspect_category': [[None, 'negative'], [None, 'conflict']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Very excited to hear that short-term Chef Jason Narone has moved on, he truly was a low point of their Sterling track record." ]
{'aspect_term': [['Chef', 'negative']], 'aspect_category': [[None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This place has good potential, but needs a significant amount of work before we can justify spending that much money on indian food you can get everywhere else." ]
{'aspect_term': [['money', 'negative'], ['indian food', 'neutral']], 'aspect_category': [[None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The wait staff is very freindly, they make it feel like you're eating in a freindly little european town." ]
{'aspect_term': [['wait staff', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Orsay, is a very pleasnt throw back to traditional French food, and French service as well." ]
{'aspect_term': [['French food', 'positive'], ['service', 'positive']], 'aspect_category': [[None, 'positive'], [None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "I really liked the noodle dishes at Rice Avenue compared to their Green Curry dish." ]
{'aspect_term': [['noodle dishes', 'positive'], ['Green Curry dish', 'negative']], 'aspect_category': [[None, 'positive'], [None, 'negative']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "This place has many different styles of pizza and they are all amazing." ]
{'aspect_term': [['styles of pizza', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "believe us, we've been eating sushi for over 15 yrs." ]
{'aspect_term': [['sushi', 'neutral']], 'aspect_category': [[None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "The place was nice and calm." ]
{'aspect_term': [['place', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "However, I was there for a work dinner not long ago when my colleague from London noticed a very large waterbug on the ceiling." ]
{'aspect_term': [['ceiling', 'negative'], ['dinner', 'neutral']], 'aspect_category': [[None, 'negative'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Their Margarita is best I've had since I've returned from Naples!" ]
{'aspect_term': [['Margarita', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "While they keep the capex to a minimum, they do put some cash into the bagels, because they among the best in the city." ]
{'aspect_term': [['capex', 'positive'], ['bagels', 'neutral']], 'aspect_category': [[None, 'positive'], [None, 'neutral']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}
generation
semeval-2014
[ "Try the crunchy tuna, it is to die for." ]
{'aspect_term': [['crunchy tuna', 'positive']], 'aspect_category': [[None, 'positive']]}
none
Task: Extracting aspect terms , their aspect categories and their corresponding sentiment polarities in a dict. Input: A sentence. Output: a dict with keys: aspect_term and aspect_category , separate extraction a list of 2-tuples(aspect term , its corresponding sentiment polarity) and a list of 2-tuples(aspect category, its corresponding sentiment polarity). Supplement: "Null" means that there is no occurrence in the sentence. Example: Input: "The Pad Thai is excellent here, as well." Output: {'aspect_term': [['Pad Thai', 'positive']], 'aspect_category': [[None, 'positive']]}