Omkar2118/datasets huggingface dataset:
FakeNewsNet
Full name: FakeNewsNet

**FakeNewsNet** is collected from two fact-checking websites: GossipCop and PolitiFact containing news contents with labels annotated by professional journalists and experts, along with social context information.

Source: [Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News](https://arxiv.org/abs/2004.01732)
Image Source: [https://arxiv.org/pdf/1809.01286.pdf](https://arxiv.org/pdf/1809.01286.pdf)

Introducing paper: FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media
SemEval-2014 Task-10

SemEval 2014 is a collection of datasets used for the Semantic Evaluation (SemEval) workshop, an annual event that focuses on the evaluation and comparison of systems that can analyze diverse semantic phenomena in text. The datasets from SemEval 2014 are used for various tasks, including but not limited to:

- **Aspect-Based Sentiment Analysis (ABSA)**: This task is based on laptop and restaurant reviews. It involves identifying the aspects or features mentioned in a review and determining the sentiment expressed towards each aspect.
- **Text Classification**: This task involves classifying text into predefined categories. Sub-tasks include text-scoring, natural language inference, and semantic-similarity-scoring.

Introducing paper: SemEval-2014 Task 10: Multilingual Semantic Textual Similarity
MEDIA
Full name: MEDIA

The **MEDIA** French corpus is dedicated to semantic extraction from speech in a context of human/machine dialogues. The corpus has manual transcription and conceptual annotation of dialogues from 250 speakers. It is split into the following three parts : (1) the training set (720 dialogues, 12K sentences), (2) the development set (79 dialogues, 1.3K sentences, and (3) the test set (200 dialogues, 3K sentences).

Source: [Dialogue history integration into end-to-end signal-to-concept spoken language understanding systems](https://arxiv.org/abs/2002.06012)
Image Source: [http://www.lrec-conf.org/proceedings/lrec2004/pdf/356.pdf](http://www.lrec-conf.org/proceedings/lrec2004/pdf/356.pdf)

Introducing paper: The French MEDIA/EVALDA Project: the Evaluation of the Understanding Capability of Spoken Language Dialogue Systems
OMICS
Full name: Open Mind Indoor Common Sense

**OMICS** is an extensive collection of knowledge for indoor service robots gathered from internet users. Currently, it contains 48 tables capturing different sorts of knowledge. Each tuple of the Help table maps a user desire to a task that may meet the desire (e.g., ⟨ “feel thirsty”, “by offering drink” ⟩). Each tuple of the Tasks/Steps table decomposes a task into several steps (e.g., ⟨ “serve a drink”, 0. “get a glass”, 1. “get a bottle”, 2. “fill class from bottle”, 3. “give class to person” ⟩). Given this, OMICS offers useful knowledge about hierarchism of naturalistic instructions, where a high-level user request (e.g., “serve a drink”) can be reduced to lower-level tasks (e.g., “get a glass”, ⋯). Another feature of OMICS is that elements of any tuple in an OMICS table are semantically related according to a predefined template. This facilitates the semantic interpretation of the OMICS tuples.

Source: [Understanding User Instructions by Utilizing Open Knowledge for Service Robots](https://arxiv.org/abs/1606.02877)
Image Source: [https://www.aaai.org/Papers/AAAI/2004/AAAI04-096.pdf](https://www.aaai.org/Papers/AAAI/2004/AAAI04-096.pdf)

Introducing paper: Common Sense Data Acquisition for Indoor Mobile Robots