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bltlab
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queryner

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train · 7.84k rows

tokens sequencelengths 2 12	ner_tags sequencelengths 2 12
[ "teeth", "whitening", "sensitive", "teeth" ]	[ 9, 10, 17, 18 ]
[ "white", "duvet", "cover", "queen" ]	[ 3, 9, 10, 1 ]
[ "eminem", "iphone", "6", "case" ]	[ 7, 25, 26, 9 ]
[ "air", "tight", "containers", "for", "food" ]	[ 17, 18, 9, 17, 18 ]
[ "2", "piece", "living", "room", "curtain" ]	[ 29, 30, 19, 20, 9 ]
[ "byron", "cage", "cd" ]	[ 7, 8, 9 ]
[ "l.o.l", "lady", "diva" ]	[ 11, 25, 26 ]
[ "meguiars", "gold", "class", "car", "wash" ]	[ 11, 25, 26, 9, 10 ]
[ "dog", "book", "for", "1", "year", "old" ]	[ 7, 9, 13, 14, 14, 14 ]
[ "caterpillar", "boots", "for", "women" ]	[ 11, 9, 13, 14 ]
[ "m37", "infiniti", "glass", "mirror", "passenger", "side" ]	[ 25, 11, 15, 9, 17, 18 ]
[ "zippo", "insert" ]	[ 11, 9 ]
[ "light", "bulb", "changer", "for", "high", "ceilings", "with", "pole" ]	[ 9, 10, 10, 17, 18, 18, 17, 18 ]
[ "heat", "protectant", "spray", "for", "hair" ]	[ 9, 10, 10, 17, 18 ]
[ "gray", "paint", "can" ]	[ 3, 9, 10 ]
[ "georgia", "sweater" ]	[ 7, 9 ]
[ "home", "is", "not", "a", "country" ]	[ 7, 8, 8, 8, 8 ]
[ "11", "pro", "max", "case", "pelican" ]	[ 25, 26, 26, 9, 11 ]
[ "billy", "club" ]	[ 25, 26 ]
[ "men’s", "dress", "socks" ]	[ 13, 9, 10 ]
[ "hanging", "wall", "planter" ]	[ 9, 10, 17 ]
[ "night", "light", "plug", "in" ]	[ 9, 10, 17, 18 ]
[ "hydrolyzed", "collagen", "iv" ]	[ 9, 10, 17 ]
[ "sunflower", "door", "handle" ]	[ 31, 9, 10 ]
[ "cardigan", "for", "men" ]	[ 9, 13, 14 ]
[ "bare", "essentials", "makeup" ]	[ 25, 26, 9 ]
[ "aquatalia", "for", "women" ]	[ 11, 13, 14 ]
[ "christmas", "fairy", "lights" ]	[ 19, 9, 10 ]
[ "trilina", "pucci", "kindle", "books" ]	[ 11, 12, 25, 9 ]
[ "gray", "christmas", "box", "signs" ]	[ 3, 19, 9, 0 ]
[ "short", "skirt" ]	[ 31, 9 ]
[ "baby", "swim", "floats", "for", "infants" ]	[ 13, 9, 10, 13, 14 ]
[ "metal", "futon", "frame", "full" ]	[ 15, 9, 10, 31 ]
[ "vibrator", "for", "clit" ]	[ 9, 17, 18 ]
[ "rv", "toilet", "bowl", "cleaner" ]	[ 17, 9, 10, 10 ]
[ "iphone", "7", "plus", "case" ]	[ 25, 26, 26, 9 ]
[ "luigis", "mansion", "toys" ]	[ 7, 8, 9 ]
[ "dust", "mask", "reusable", "with", "alien" ]	[ 9, 10, 17, 7, 8 ]
[ "ballon", "garland", "kits" ]	[ 9, 10, 10 ]
[ "toothbrush", "holder", "wall", "mount" ]	[ 9, 10, 17, 18 ]
[ "iphone", "5" ]	[ 25, 26 ]
[ "saucony", "jazz", "mens", "olive" ]	[ 25, 26, 13, 3 ]
[ "yellow", "pepper" ]	[ 9, 10 ]
[ "crazy", "aarons", "thinking", "putty" ]	[ 11, 12, 25, 26 ]
[ "batgirl", "costume", "for", "women" ]	[ 7, 9, 13, 14 ]
[ "woman", "and", "daughter", "book" ]	[ 7, 8, 8, 9 ]
[ "glass", "jars", "with", "lids" ]	[ 15, 9, 31, 32 ]
[ "10", "inch", "bookshelf" ]	[ 1, 2, 9 ]
[ "goalzero", "yeti", "150", "charger" ]	[ 11, 25, 26, 9 ]
[ "toyota", "yaris", "bumper", "clips" ]	[ 11, 17, 9, 10 ]
[ "iron", "man", "costume" ]	[ 7, 8, 9 ]
[ "panacur", "for", "cats" ]	[ 9, 17, 18 ]
[ "bella", "canvas", "shirt", "triblend" ]	[ 11, 12, 9, 17 ]
[ "3/8", "id", "vinyl", "tubing" ]	[ 1, 2, 15, 9 ]
[ "cruelty", "free", "moisturizer", "face" ]	[ 17, 18, 9, 10 ]
[ "for", "him" ]	[ 13, 14 ]
[ "dual", "tip", "alcohol", "markers" ]	[ 17, 18, 15, 9 ]
[ "sink", "strainer" ]	[ 9, 10 ]
[ "motorcycle", "keychain" ]	[ 31, 9 ]
[ "mini", "figures" ]	[ 31, 9 ]
[ "versa", "fitbits" ]	[ 25, 11 ]
[ "4k", "gopro" ]	[ 17, 11 ]
[ "womans", "tricycle", "adult", "bike" ]	[ 13, 9, 13, 9 ]
[ "lace", "up", "shoes", "for", "women", "flats", "comfortable" ]	[ 31, 32, 9, 13, 14, 31, 17 ]
[ "sling", "backpack" ]	[ 31, 9 ]
[ "mini", "tassels", "gold" ]	[ 31, 9, 3 ]
[ "tree", "costume", "adult" ]	[ 31, 9, 13 ]
[ "john", "deere", "set" ]	[ 11, 12, 9 ]
[ "lg", "k20", "plus" ]	[ 11, 25, 26 ]
[ "feminization", "transformation" ]	[ 0, 0 ]
[ "boys", "robes", "size", "6-7" ]	[ 13, 9, 1, 2 ]
[ "super", "gumball", "toestop", "short" ]	[ 25, 26, 9, 10 ]
[ "joaquin", "phoenix", "joker", "poster" ]	[ 7, 8, 8, 9 ]
[ "golden", "sand", "oil" ]	[ 25, 26, 9 ]
[ "captain", "toad", "treasure", "tracker", "switch" ]	[ 7, 8, 8, 8, 25 ]
[ "college", "crewneck", "sweatshirt" ]	[ 7, 31, 9 ]
[ "avery", "white", "binder" ]	[ 11, 3, 9 ]
[ "ethernet", "splitter" ]	[ 9, 10 ]
[ "short", "green", "wig" ]	[ 31, 3, 9 ]
[ "halloween", "lamp", "decorations" ]	[ 19, 9, 17 ]
[ "fashion", "fair" ]	[ 11, 12 ]
[ "iphone", "11", "casemate", "case" ]	[ 25, 26, 11, 9 ]
[ "nike", "womens", "shoe", "9.5" ]	[ 11, 13, 9, 1 ]
[ "milk", "carton", "water", "bottles" ]	[ 31, 32, 9, 10 ]
[ "long", "high", "low", "shirts" ]	[ 1, 31, 32, 9 ]
[ "bed", "frame", "that", "requires", "a", "box", "spring" ]	[ 9, 10, 17, 18, 18, 18, 18 ]
[ "broadway", "dvd" ]	[ 7, 9 ]
[ "led", "spot", "lights", "outdoor" ]	[ 17, 9, 10, 17 ]
[ "lavento", "womens", "high", "waisted", "yoga", "shorts" ]	[ 11, 13, 31, 32, 9, 10 ]
[ "babyzen", "travel", "bag" ]	[ 11, 9, 10 ]
[ "mercedes", "slk", "350", "exterior", "accessories" ]	[ 17, 18, 18, 18, 9 ]
[ "custom", "iphone", "7\\8", "case" ]	[ 17, 25, 26, 9 ]
[ "hey", "dude", "womens" ]	[ 11, 12, 13 ]
[ "ram", "stick", "16", "gb" ]	[ 9, 10, 1, 2 ]
[ "products", "for", "newborns" ]	[ 9, 13, 14 ]
[ "dragon", "ball", "z", "clothes" ]	[ 7, 8, 8, 9 ]
[ "self", "tanner", "spray" ]	[ 9, 10, 10 ]
[ "ninja", "custom", "for", "boys" ]	[ 7, 9, 13, 14 ]
[ "laptop", "cooler" ]	[ 17, 9 ]
[ "kettlebells", "set" ]	[ 11, 9 ]

End of preview. Expand in Dataset Viewer.

Dataset Card for QueryNER

QueryNER is a sequence labeling dataset for e-commerce query segmentation. It has 17 different entity types. QueryNER covers nearly the entire query rather than just certain key aspects that may be covered by other aspect-value extraction systems.

Dataset Details

Dataset Description

QueryNER is a manually-annotated dataset and accompanying model for e-commerce query segmentation. Prior work in sequence labeling for e-commerce has largely addressed aspect-value extraction which focuses on extracting portions of a product title or query for narrowly defined aspects. Our work instead focuses on the goal of dividing a query into meaningful chunks with broadly applicable types. QueryNER has 17 different entity types.

Repository: QueryNER
Paper: Accepted at LREC-COLING 2024, coming soon
Curated by: BLT Lab
Language(s) (NLP): English
License: CC-BY 4.0

Dataset Sources

QueryNER is annotation on a subsection of Amazon's ESCI Shopping Queries dataset.

Uses

QueryNER is intended to be used for segmentation of e-commerce queries in English.

Direct Use

QueryNER can be used for research on e-commerce query segmentation. It may also be used for e-commerce query segmentation for use in further downstream systems; however, we caution users that while the ontology is broadly applicable, using models trained on only this small public release may have suboptimal performance especially on out of domain data.

Out-of-Scope Use

Users would likely experience poor segmentation performance on data outside of the e-commerce domain. Because the dataset is on the smaller side, additional annotated data on additional data using the QueryNER ontology may be necessary to get better performance on other datasets.

Dataset Structure

The dataset includes the query tokens and their tags.

Dataset Creation

See paper.

Curation Rationale

The dataset was created for research and for downstream applications for e-commerce search systems to make use of segmented queries.

Source Data

The source data is from the Shopping Queries ESCI dataset. https://github.com/amazon-science/esci-data

Data Collection and Processing

See paper

Who are the source data producers?

See source data repo and paper.

Annotations

Annotation process

See paper for details.

Who are the annotators?

Annotators were contract workers and were paid a living wage.

Personal and Sensitive Information

The dataset is just user e-commerce queries and should not contain any sensitive information.

Bias, Risks, and Limitations

The dataset is English only for now. Bias may be toward e-commerce queries of the source data. There may also be annotator bias since the dataset is annotated by a single annotator for the training set and three annotators and an adjudicator for the development and test sets.

Citation

To appear at LREC-COLING 2024.

BibTeX:

@misc{palenmichel2024queryner,
      title={QueryNER: Segmentation of E-commerce Queries}, 
      author={Chester Palen-Michel and Lizzie Liang and Zhe Wu and Constantine Lignos},
      year={2024},
      eprint={2405.09507},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}