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Visual Pair Linguistic Nuance and Comparison Data
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Differentiating between visually similar objects requires specific linguistic nuances and an awareness of physical attributes. This collection captures the way individuals describe and distinguish between pairs of items through short, descriptive sentences. Contributors were presented with pairs where one item was consistently smaller than the other and were tasked with writing unique descriptions that highlighted the differences between the two, rather than using generic labels. This provides a valuable look at human perception and the descriptive choices made when comparing common items like food or household objects.
Columns
- _unit_id: A unique numerical identifier assigned to each individual image pair.
- _unit_state: The processing state of the image pair, which remains "finalized" across the entire collection.
- _trusted_judgments: The total number of verified human assessments recorded for each specific pair.
- _last_judgment_at: The specific timestamp recording when the final description for the pair was submitted.
- keyword_1: A description of the first object in the pair, consisting of three sentences aimed at distinguishing it from its partner.
- keyword_2: A description of the second, smaller object in the pair, also provided in a three-sentence format.
- image_url: The web address linking to the specific image containing the pair of objects being described.
Distribution
The data is delivered in a CSV format titled
free-textobject-pair-descriptions-DFE.csv with a file size of 280.16 kB. It contains 1,225 valid records structured across 7 distinct columns. The resource maintains high integrity with a 100% validity rate and no missing or mismatched entries. It has been assigned a top-tier usability rating of 10.00 and is a static resource with no future updates expected.Usage
This collection is ideal for training natural language processing models to generate comparative descriptions or to identify spatial and physical relationships between objects. Researchers can utilise the free-text entries to study how humans prioritise certain features, such as colour or shape, when forced to differentiate between two similar items. Furthermore, it serves as a foundation for multi-modal machine learning projects that seek to bridge the gap between computer vision and descriptive linguistics.
Coverage
The temporal scope of the data is concentrated on 23 March 2015, which is when the judgments were finalised. The geographic and demographic scope involves the contributors who participated in the descriptive survey, providing 1,225 unique image pairings. The visual content largely focuses on common objects and food items, with a specific focus on relative size as a distinguishing factor.
License
CC0: Public Domain
Who Can Use It
Computational linguists and AI researchers can leverage these descriptions to improve the nuance of automated image captioning systems. Psychology students and academics may find the data useful for exploring cognitive biases in how people perceive and describe size differences. Additionally, data scientists can use the structured text to practice sentiment analysis or keyword extraction within a comparative framework.
Dataset Name Suggestions
- Comparative Linguistic Descriptions of Object Pairs
- Human-Generated Object Differentiation and Description Registry
- Visual Pair Linguistic Nuance and Comparison Data
- Textual Descriptions for Relative Object Sizing
Attributes
Original Data Source: Visual Pair Linguistic Nuance and Comparison Data
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