Datasets:
id
int64 0
2k
| tokens
sequence | ner_tags
sequence |
---|---|---|
0 | [
"Judging",
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"posts",
"this",
"used",
"to",
"be",
"a",
"good",
"place",
",",
"but",
"not",
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"We,",
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"us,",
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"at",
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"-",
"the",
"place",
"was",
"empty",
"-",
"and",
"the",
"staff",
"acted",
"like",
"we",
"were",
"imposing",
"on",
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"and",
"they",
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"very",
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"They",
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"noodles,",
"ignored",
"repeated",
"requests",
"for",
"sugar,",
"and",
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"our",
"dishes",
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"The",
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"was",
"lousy",
"-",
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"or",
"too",
"salty",
"and",
"the",
"portions",
"tiny."
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4 | [
"After",
"all",
"that,",
"they",
"complained",
"to",
"me",
"about",
"the",
"small",
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5 | [
"Avoid",
"this",
"place",
"!"
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6 | [
"I",
"have",
"eaten",
"at",
"Saul,",
"many",
"times,",
"the",
"food",
"is",
"always",
"consistently,",
"outrageously",
"good."
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7 | [
"Saul",
"is",
"the",
"best",
"restaurant",
"on",
"Smith",
"Street",
"and",
"in",
"Brooklyn."
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8 | [
"The",
"duck",
"confit",
"is",
"always",
"amazing",
"and",
"the",
"foie",
"gras",
"terrine",
"with",
"figs",
"was",
"out",
"of",
"this",
"world."
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9 | [
"The",
"wine",
"list",
"is",
"interesting",
"and",
"has",
"many",
"good",
"values."
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10 | [
"For",
"the",
"price,",
"you",
"cannot",
"eat",
"this",
"well",
"in",
"Manhattan."
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11 | [
"I",
"was",
"very",
"disappointed",
"with",
"this",
"restaurant",
"."
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12 | [
"Ive",
"asked",
"a",
"cart",
"attendant",
"for",
"a",
"lotus",
"leaf",
"wrapped",
"rice",
"and",
"she",
"replied",
"back",
"rice",
"and",
"just",
"walked",
"away."
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13 | [
"I",
"had",
"to",
"ask",
"her",
"three",
"times",
"before",
"she",
"finally",
"came",
"back",
"with",
"the",
"dish",
"Ive",
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14 | [
"Food",
"was",
"okay,",
"nothing",
"great."
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15 | [
"Chow",
"fun",
"was",
"dry;",
"pork",
"shu",
"mai",
"was",
"more",
"than",
"usually",
"greasy",
"and",
"had",
"to",
"share",
"a",
"table",
"with",
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16 | [
"I/we",
"will",
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"to",
"this",
"place",
"again."
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17 | [
"Went",
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"3",
"day",
"oyster",
"binge,",
"with",
"Fish",
"bringing",
"up",
"the",
"closing,",
"and",
"I",
"am",
"so",
"glad",
"this",
"was",
"the",
"place",
"it",
"O",
"trip",
"ended,",
"because",
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"was",
"so",
"great!"
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18 | [
"Service",
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"oysters",
"where",
"a",
"sensual",
"as",
"they",
"come,",
"and",
"the",
"price",
"can't",
"be",
"beat!!!"
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19 | [
"You",
"can't",
"go",
"wrong",
"here."
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20 | [
"Every",
"time",
"in",
"New",
"York",
"I",
"make",
"it",
"a",
"point",
"to",
"visit",
"Restaurant",
"Saul",
"on",
"Smith",
"Street."
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21 | [
"Everything",
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"cooked",
"to",
"perfection,",
"the",
"service",
"is",
"excellent,",
"the",
"decor",
"cool",
"and",
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22 | [
"I",
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"the",
"duck",
"breast",
"special",
"on",
"my",
"last",
"visit",
"and",
"it",
"was",
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23 | [
"Can't",
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24 | [
"I",
"had",
"my",
"eyes",
"on",
"this",
"place,",
"promising",
"myself",
"I",
"will",
"one",
"day",
"'give",
"it",
"a",
"try.'"
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25 | [
"And",
"I",
"hate",
"to",
"say",
"this",
"but",
"I",
"doubt",
"I'll",
"ever",
"go",
"back."
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26 | [
"The",
"food",
"is",
"very",
"average...the",
"Thai",
"fusion",
"stuff",
"is",
"a",
"bit",
"too",
"sweet,",
"every",
"thing",
"they",
"serve",
"is",
"too",
"sweet",
"here."
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27 | [
"The",
"only",
"thing",
"I",
"moderately",
"enjoyed",
"was",
"their",
"Grilled",
"Chicken",
"special",
"with",
"Edamame",
"Puree",
"."
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28 | [
"I",
"had",
"never",
"had",
"Edamame",
"pureed",
"before",
"but",
"I",
"thought",
"it",
"was",
"innovative",
"and",
"tasty",
"(could've",
"used",
"a",
"bit",
"more",
"salt)."
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29 | [
"Anyways,",
"if",
"you're",
"in",
"the",
"neighborhood",
"to",
"eat",
"good",
"food,",
"I",
"wouldn't",
"waste",
"my",
"time",
"trying",
"to",
"find",
"something,",
"rather",
"go",
"across",
"the",
"street",
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30 | [
"The",
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"is",
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"they",
"REALLY",
"need",
"to",
"clean",
"that",
"vent",
"in",
"the",
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"quite",
"un-appetizing,",
"and",
"kills",
"your",
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"make",
"this",
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31 | [
"We",
"ate",
"outside",
"at",
"Haru's",
"Sake",
"bar",
"because",
"Haru's",
"restaurant",
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33 | [
"Their",
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"list",
"was",
"extensive,",
"but",
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"were",
"looking",
"for",
"Purple",
"Haze,",
"which",
"wasn't",
"listed",
"but",
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34 | [
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"roll",
"was",
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"good",
"and",
"the",
"rock",
"shrimp",
"tempura",
"was",
"awesome,",
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35 | [
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"around",
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"on",
"a",
"Friday",
"and",
"it",
"had",
"died",
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"pony",
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37 | [
"THe",
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38 | [
"Food",
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39 | [
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40 | [
"Ambiance",
"-",
"relaxed",
"and",
"stylish."
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41 | [
"Don't",
"judge",
"this",
"place",
"prima",
"facie,",
"you",
"have",
"to",
"try",
"it",
"to",
"believe",
"it,",
"a",
"home",
"away",
"from",
"home",
"for",
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42 | [
"i",
"have",
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"here",
"a",
"handful",
"of",
"times,",
"for",
"no",
"reason",
"besides",
"sheer",
"convenience."
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43 | [
"(i",
"hang",
"out,",
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"live,",
"in",
"the",
"neighborhood..)"
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44 | [
"the",
"food",
"is",
"decent."
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45 | [
"however,",
"it's",
"the",
"service",
"that",
"leaves",
"a",
"bad",
"taste",
"in",
"my",
"mouth."
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46 | [
"i",
"happen",
"to",
"have",
"a",
"policy",
"that",
"goes",
"along",
"with",
"a",
"little",
"bit",
"of",
"self-respect,",
"which",
"includes",
"not",
"letting",
"a",
"waiter",
"intimidate",
"me,",
"i.e.",
"make",
"me",
"feel",
"bad",
"asking",
"for",
"trivialities",
"like",
"water,",
"or",
"the",
"check."
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47 | [
"i",
"know,",
"you",
"were",
"too",
"busy",
"showing",
"off",
"your",
"vintage",
"tee",
"shirt",
"and",
"looking",
"bored,",
"but",
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"enjoy",
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"company",
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"friends,",
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48 | [
"well,",
"i",
"didn't",
"find",
"it",
"there,",
"and",
"trust,",
"i",
"have",
"told",
"everyone",
"i",
"can",
"think",
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"about",
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"experience."
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49 | [
"the",
"last",
"time",
"i",
"walked",
"by",
"it",
"looked",
"pretty",
"empty.",
"hmmm."
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50 | [
"This",
"place",
"has",
"got",
"to",
"be",
"the",
"best",
"japanese",
"restaurant",
"in",
"the",
"new",
"york",
"area."
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51 | [
"I",
"had",
"a",
"great",
"experience."
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0,
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0,
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0
] |
52 | [
"Food",
"is",
"great."
] | [
1,
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0
] |
53 | [
"Service",
"is",
"top",
"notch."
] | [
1,
0,
0,
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] |
54 | [
"I",
"have",
"been",
"going",
"back",
"again",
"and",
"again."
] | [
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55 | [
"I",
"tend",
"to",
"judge",
"a",
"sushi",
"restaurant",
"by",
"its",
"sea",
"urchin",
",",
"which",
"was",
"heavenly",
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56 | [
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65 | [
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69 | [
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77 | [
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79 | [
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80 | [
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81 | [
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83 | [
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84 | [
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85 | [
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86 | [
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90 | [
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91 | [
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92 | [
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93 | [
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94 | [
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95 | [
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98 | [
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End of preview. Expand
in Dataset Viewer.
This repository contains the English 'SemEval-2014 Task 4: Aspect Based Sentiment Analysis'. translated with DeepL into Spanish, French, Russian, and Turkish. The labels have been manually projected. For more details, read this paper: Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings.
Intended Usage: Since the datasets are parallel across languages, they are ideal for evaluating annotation projection algorithms, such as T-Projection.
Label Dictionary
{
"O": 0,
"B-TARGET": 1,
"I-TARGET": 2
}
Cication
If you use this data, please cite the following papers:
@inproceedings{garcia-ferrero-etal-2022-model,
title = "Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings",
author = "Garc{\'\i}a-Ferrero, Iker and
Agerri, Rodrigo and
Rigau, German",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.478",
doi = "10.18653/v1/2022.findings-emnlp.478",
pages = "6403--6416",
abstract = "Zero-resource cross-lingual transfer approaches aim to apply supervised modelsfrom a source language to unlabelled target languages. In this paper we performan in-depth study of the two main techniques employed so far for cross-lingualzero-resource sequence labelling, based either on data or model transfer. Although previous research has proposed translation and annotation projection(data-based cross-lingual transfer) as an effective technique for cross-lingualsequence labelling, in this paper we experimentally demonstrate that highcapacity multilingual language models applied in a zero-shot (model-basedcross-lingual transfer) setting consistently outperform data-basedcross-lingual transfer approaches. A detailed analysis of our results suggeststhat this might be due to important differences in language use. Morespecifically, machine translation often generates a textual signal which isdifferent to what the models are exposed to when using gold standard data,which affects both the fine-tuning and evaluation processes. Our results alsoindicate that data-based cross-lingual transfer approaches remain a competitiveoption when high-capacity multilingual language models are not available.",
}
@inproceedings{pontiki-etal-2014-semeval,
title = "{S}em{E}val-2014 Task 4: Aspect Based Sentiment Analysis",
author = "Pontiki, Maria and
Galanis, Dimitris and
Pavlopoulos, John and
Papageorgiou, Harris and
Androutsopoulos, Ion and
Manandhar, Suresh",
editor = "Nakov, Preslav and
Zesch, Torsten",
booktitle = "Proceedings of the 8th International Workshop on Semantic Evaluation ({S}em{E}val 2014)",
month = aug,
year = "2014",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S14-2004",
doi = "10.3115/v1/S14-2004",
pages = "27--35",
}
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