Omkar2118/datasets huggingface dataset:
ACE 2005
Full name: ACE 2005 Multilingual Training Corpus

**ACE 2005** Multilingual Training Corpus contains the complete set of English, Arabic and Chinese training data for the 2005 Automatic Content Extraction (ACE) technology evaluation. The corpus consists of data of various types annotated for entities, relations and events by the Linguistic Data Consortium (LDC) with support from the ACE Program and additional assistance from LDC.

Source: [https://catalog.ldc.upenn.edu/LDC2006T06](https://catalog.ldc.upenn.edu/LDC2006T06)
Image Source: [https://arxiv.org/pdf/1811.06031.pdf](https://arxiv.org/pdf/1811.06031.pdf)
SemEval-2014 Task-4

Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service). By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect. Datasets consisting of customer reviews with human-authored annotations identifying the mentioned aspects of the target entities and the sentiment polarity of each aspect will be provided.

***Subtask 2: Aspect term polarity***

For a given set of aspect terms within a sentence, determine whether the polarity of each aspect term is positive, negative, neutral or conflict (i.e., both positive and negative).

For example:

“I loved their fajitas” → {fajitas: positive}
“I hated their fajitas, but their salads were great” → {fajitas: negative, salads: positive}
“The fajitas are their first plate” → {fajitas: neutral}
“The fajitas were great to taste, but not to see” → {fajitas: conflict}

Introducing paper: SemEval-2014 Task 4: Aspect Based Sentiment Analysis
MR
Full name: MR Movie Reviews

**MR** Movie Reviews is a dataset for use in sentiment-analysis experiments. Available are collections of movie-review documents labeled with respect to their overall sentiment polarity (positive or negative) or subjective rating (e.g., "two and a half stars") and sentences labeled with respect to their subjectivity status (subjective or objective) or polarity.

Source: [http://www.cs.cornell.edu/people/pabo/movie-review-data/](http://www.cs.cornell.edu/people/pabo/movie-review-data/)
Image Source: [https://storage.googleapis.com/kaggle-competitions/kaggle/3810/media/treebank.png](https://storage.googleapis.com/kaggle-competitions/kaggle/3810/media/treebank.png)
STS Benchmark
Full name: Semantic Textual Similarity

STS Benchmark comprises a selection of the English datasets used in the STS tasks organized in the context of SemEval between 2012 and 2017. The selection of datasets include text from image captions, news headlines and user forums.

Source: [STS Benchmark](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark)
Yahoo! Answers

The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. Each class contains 140,000 training samples and 6,000 testing samples. Therefore, the total number of training samples is 1,400,000 and testing samples 60,000 in this dataset. From all the answers and other meta-information, we only used the best answer content and the main category information.
Source:[github](https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset/tree/master/dataset)

Introducing paper: Character-level Convolutional Networks for Text Classification
Resume NER
Full name: Resume NER

Resume contains eight fine-grained entity categories -score from 74.5% to 86.88%.

Source: [Query-Based Named Entity Recognition](https://arxiv.org/abs/1908.09138)
Image Source: [https://arxiv.org/pdf/1805.02023.pdf](https://arxiv.org/pdf/1805.02023.pdf)

Introducing paper: Chinese NER Using Lattice LSTM
TACRED
Full name: The TAC Relation Extraction Dataset

TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC KBP challenges and crowdsourcing.

Source: https://nlp.stanford.edu/projects/tacred/

Introducing paper: Position-aware Attention and Supervised Data Improve Slot Filling