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id (int32)sentence (string)motion (string)motion_entities (list)
0
" A little boy holding a yellow ball walks by."
"yes"
[ { "entity": "little boy", "start_index": 2 }, { "entity": "ball", "start_index": 30 } ]
1
" The camel walks as the woman leans forward."
"yes"
[ { "entity": "camel", "start_index": 4 }, { "entity": "woman", "start_index": 23 } ]
2
" The man mixes up various ingredients and begins laying plaster on the floor."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "plaster", "start_index": 55 } ]
3
" He continues laying tiles on the floor while looking back to speak to the camera."
"yes"
[ { "entity": "He", "start_index": 0 }, { "entity": "tiles", "start_index": 20 } ]
4
"Then one man stands in a field holding a wooden object and begins twisting it."
"yes"
[ { "entity": "man", "start_index": 9 }, { "entity": "wooden object", "start_index": 41 } ]
5
"He then bends down and grabs a ball."
"yes"
[ { "entity": "He", "start_index": 0 }, { "entity": "ball", "start_index": 31 } ]
6
"After,the ball is placed on the ground and he picks it up and hits it as if he's playing baseball."
"yes"
[ { "entity": "he", "start_index": 43 }, { "entity": "ball", "start_index": 10 } ]
7
"After,everyone is pictured lying down on the ground as if they are dead but one person begins to sit up but gets hit in the head by the ball and lays back down."
"yes"
[ { "entity": "person", "start_index": 80 }, { "entity": "ball", "start_index": 136 } ]
8
" She lays out wrapping paper, showing how to wrap a toy in it."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "wrapping paper", "start_index": 13 } ]
9
" She wraps it around the toy, then tapes it up."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "toy", "start_index": 24 } ]
10
"A camera pans over a snowy area and leads into a man standing on a snowboard and riding down a mountain."
"yes"
[ { "entity": "man", "start_index": 49 }, { "entity": "camera", "start_index": 2 } ]
11
"The woman picks up a clipboard next to her and smooths out the papers that are on top of it while smiling, crosses her legs and sits back."
"yes"
[ { "entity": "woman", "start_index": 4 }, { "entity": "clipboard", "start_index": 21 }, { "entity": "papers", "start_index": 63 } ]
12
" When the clips end the man and woman on the couch begin talking and she puts her clipboard down."
"yes"
[ { "entity": "she", "start_index": 68 }, { "entity": "man", "start_index": 23 } ]
13
" The man then shares a beer with other people around him."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "beer", "start_index": 22 } ]
14
"A basketball player is seen dribbling a ball in various shots for the camera and leads into the player making baskets over and over again."
"yes"
[ { "entity": "player", "start_index": 13 }, { "entity": "ball", "start_index": 40 } ]
15
" The video includes numerous clips of players in lacrosse games, hitting the ball toward opposing goals."
"yes"
[ { "entity": "players", "start_index": 37 }, { "entity": "ball", "start_index": 76 } ]
16
"A man is playing the bagpipes in front of people."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "bagpipes", "start_index": 21 } ]
17
"A man is seen padding a canoe along the water while the camera captures him from several angles."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "canoe", "start_index": 24 } ]
18
" The blackjack attendant places cards on the table."
"yes"
[ { "entity": "blackjack attendant", "start_index": 4 }, { "entity": "cards", "start_index": 31 } ]
19
"Two men in padded sumo costumes are pulled along a tug rope on a ski slope."
"yes"
[ { "entity": "men", "start_index": 4 }, { "entity": "rope", "start_index": 55 } ]
20
" Two men in yellow padded sumo costumes do jumps on their snowboard on a downhill course."
"yes"
[ { "entity": "men", "start_index": 4 }, { "entity": "snowboard", "start_index": 57 } ]
21
" A man on small skis crashes while trying to jump."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "skis", "start_index": 15 } ]
22
"A close up of a bike wheel is seen that leads into a person's hands pushing on the tire."
"yes"
[ { "entity": "hands", "start_index": 62 }, { "entity": "tire", "start_index": 83 } ]
23
" The person uses a tool along the tire to help move it along."
"yes"
[ { "entity": "person", "start_index": 4 }, { "entity": "tool", "start_index": 18 } ]
24
" The person finally pushes the tire along it's sides."
"yes"
[ { "entity": "person", "start_index": 4 }, { "entity": "tire", "start_index": 30 } ]
25
" She ready's the tip of a glass and dips the glass into sugar on a plate."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "glass", "start_index": 25 } ]
26
" She pours various liquids into a mixer and shakes the mixture all together."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "liquids", "start_index": 18 }, { "entity": "mixture", "start_index": 54 } ]
27
" She pours the drink out into the glass while still speaking to the camera."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "drink", "start_index": 14 } ]
28
" A person is mixing salad ingredients in a large bowl."
"yes"
[ { "entity": "person", "start_index": 2 }, { "entity": "ingredients", "start_index": 25 } ]
29
" Two customers make a purchase and give money to the seller and give the camera a thumbs up."
"yes"
[ { "entity": "Two customers", "start_index": 0 }, { "entity": "money", "start_index": 39 } ]
30
" The chef chops some cilantro on the kitchen counter."
"yes"
[ { "entity": "chef", "start_index": 4 }, { "entity": "cilantro", "start_index": 20 } ]
31
" Then the chef places the ingredients in a salad bowl."
"yes"
[ { "entity": "chef", "start_index": 9 }, { "entity": "ingredients", "start_index": 25 } ]
32
" They strain the juice and pour it into a jar."
"yes"
[ { "entity": "They", "start_index": 0 }, { "entity": "juice", "start_index": 16 } ]
33
" They put a straw and a lime wedge on the rim of the glass."
"yes"
[ { "entity": "They", "start_index": 0 }, { "entity": "straw", "start_index": 11 } ]
34
" The boy turns on the sink faucet."
"yes"
[ { "entity": "boy", "start_index": 4 }, { "entity": "sink faucet", "start_index": 21 } ]
35
"A dealer is shown laying out cards on a table while other people's hands are shown on the side."
"yes"
[ { "entity": "dealer", "start_index": 2 }, { "entity": "cards", "start_index": 29 } ]
36
" The person then lays out cards while another person lays out their chips."
"yes"
[ { "entity": "person", "start_index": 4 }, { "entity": "cards", "start_index": 25 }, { "entity": "chips", "start_index": 67 } ]
37
"After the crowd is shown and more people are seen running through the city or even participating in a wheel chair as the people on the side cheer them on."
"yes"
[ { "entity": "people", "start_index": 34 }, { "entity": "wheel chair", "start_index": 102 } ]
38
" He speaks some more and shows his harmonica again as the video goes to an end title screen that reads Howcast original"."
"yes"
[ { "entity": "He", "start_index": 0 }, { "entity": "harmonica", "start_index": 34 } ]
39
"A small group of people are seen wandering around a tennis court hitting a ball over the net."
"yes"
[ { "entity": "people", "start_index": 17 }, { "entity": "ball", "start_index": 75 } ]
40
" She places a piece of bubblewrap and makes sure it covers around about half of the wood."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "bubblewrap", "start_index": 22 } ]
41
" She then covers the rest of the wood and walks across it like a plank."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "wood", "start_index": 32 } ]
42
"A man uses a large plastic blue bat."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "bat", "start_index": 32 } ]
43
"Athletes throw a javelin during a competition."
"yes"
[ { "entity": "Athletes", "start_index": 0 }, { "entity": "javelin", "start_index": 17 } ]
44
" A man practices his throwing stance walking and doing half throws holding a javeline."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "javeline", "start_index": 76 } ]
45
" The man swings a hammer."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "hammer", "start_index": 17 } ]
46
" The man throws a weighted ball."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "ball", "start_index": 26 } ]
47
"We see a person sharpen a knife on a sanding disc."
"yes"
[ { "entity": "person", "start_index": 9 }, { "entity": "knife", "start_index": 26 } ]
48
" A woman is outside, shoveling snow."
"yes"
[ { "entity": "woman", "start_index": 2 }, { "entity": "snow", "start_index": 30 } ]
49
"They put the wheels back onto the bike."
"yes"
[ { "entity": "They", "start_index": 0 }, { "entity": "wheels", "start_index": 13 } ]
50
" The man stand with the hands on the border on the equipment and descend down the walk with the hands, then he walk on hands sideward."
"yes"
[ { "entity": "The man", "start_index": 0 }, { "entity": "hands", "start_index": 23 } ]
51
"A large group of people are seen sitting around an auditorium with one man flying kites around in the middle."
"yes"
[ { "entity": "man", "start_index": 71 }, { "entity": "kites", "start_index": 82 } ]
52
" The person continues moving around with the kites and walks away in the end with a woman speaking to the crowd."
"yes"
[ { "entity": "The person", "start_index": 0 }, { "entity": "kites", "start_index": 44 } ]
53
" A person is using a leaf blower to blow leaves into a pile."
"yes"
[ { "entity": "person", "start_index": 2 }, { "entity": "leaf blower", "start_index": 20 } ]
54
"The person holding the camera starts his journey and he is off down the slopes of snow."
"yes"
[ { "entity": "person", "start_index": 4 }, { "entity": "camera", "start_index": 23 } ]
55
"A camera zooms in on a person riding in a tube."
"yes"
[ { "entity": "person", "start_index": 23 }, { "entity": "camera", "start_index": 2 } ]
56
" The boy continues playing the instrument while the camera zooms in on him playing."
"yes"
[ { "entity": "boy", "start_index": 4 }, { "entity": "camera", "start_index": 51 } ]
57
"A man plays guitar and harmonica at the same time."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "guitar", "start_index": 12 } ]
58
" After, the man continues playing both the guitar and the harmonica, then sing."
"yes"
[ { "entity": "man", "start_index": 11 }, { "entity": "guitar", "start_index": 42 }, { "entity": "harmonica", "start_index": 57 } ]
59
" At one point, his friends assist with the jump rope tricks."
"yes"
[ { "entity": "friends", "start_index": 18 }, { "entity": "rope", "start_index": 47 } ]
60
"A lady is seen putting wax on a mans shoes."
"yes"
[ { "entity": "lady", "start_index": 2 }, { "entity": "wax", "start_index": 23 } ]
61
" The woman rubs the mans shoes with a cloth."
"yes"
[ { "entity": "woman", "start_index": 4 }, { "entity": "shoes", "start_index": 24 } ]
62
"Next,the truck is opened,and the person removes the spare tire and the jack."
"yes"
[ { "entity": "person", "start_index": 33 }, { "entity": "truck", "start_index": 9 }, { "entity": "tire", "start_index": 58 } ]
63
"He then lifts the car and continues to loosen the bolts on the wheel of the car."
"yes"
[ { "entity": "He", "start_index": 0 }, { "entity": "car", "start_index": 18 }, { "entity": "bolts", "start_index": 50 } ]
64
"Once loose,the wheel is removed and the spare is put on and the same actions are performed in reverse."
"yes"
[ { "entity": "the spare", "start_index": 36 }, { "entity": "the wheel", "start_index": 11 } ]
65
" Onlookers pass by while the group continues to dance."
"yes"
[ { "entity": "Onlookers", "start_index": 0 }, { "entity": "the group", "start_index": 24 } ]
66
" The man strum the other drum while moving his leg."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "drum", "start_index": 24 } ]
67
" The woman placed the violin between her neck and shoulder and began strumming the violin with the stick."
"yes"
[ { "entity": "woman", "start_index": 4 }, { "entity": "violin", "start_index": 21 } ]
68
" The lady put the violin on her lap and talked."
"yes"
[ { "entity": "lady", "start_index": 4 }, { "entity": "violin", "start_index": 17 } ]
69
" A man in a black shirt moves a light around."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "light", "start_index": 31 } ]
70
" The hair stylists rubs some pomade on the man's hair."
"yes"
[ { "entity": "stylists", "start_index": 9 }, { "entity": "pomade", "start_index": 28 } ]
71
" She is holding a baton and twirling it in her hands."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "baton", "start_index": 17 } ]
72
" The woman continues washing silverware while looking over to the camera and smiling."
"yes"
[ { "entity": "woman", "start_index": 4 }, { "entity": "silverware", "start_index": 28 } ]
73
" The man completes the jump after a couple tries and is shown again in slow motion."
"yes"
[ { "entity": "The man", "start_index": 0 }, { "entity": "couple", "start_index": 35 } ]
74
" Then she takes a nail file and files the ends of the nails to smooth it."
"yes"
[ { "entity": "she", "start_index": 5 }, { "entity": "file", "start_index": 22 } ]
75
" She also uses some lime green nail polish and paints the nails to finish off the process and make them shiny."
"yes"
[ { "entity": "She", "start_index": 0 }, { "entity": "nail polish", "start_index": 30 } ]
76
" The man creates a giant omelette, finally flipping it onto a plate and handing it to a patron."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "giant omelette", "start_index": 18 } ]
77
"An old man with gray hair, wearing red shirt and black pants is dribbling the ball in an indoor court, he dribbled the ball side by side, between his legs."
"yes"
[ { "entity": "old man", "start_index": 3 }, { "entity": "ball", "start_index": 78 } ]
78
" He shoot the ball into the ring, then start dribbling again to side to side and between his legs, and shoot the ball into the ring."
"yes"
[ { "entity": "He", "start_index": 0 }, { "entity": "ball", "start_index": 13 } ]
79
" A helicopter rides over the shoreline where the surfers are riding."
"yes"
[ { "entity": "surfers", "start_index": 48 }, { "entity": "helicopter", "start_index": 2 } ]
80
" A man is then shown prepping his board with surf wax."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "board", "start_index": 33 } ]
81
" The crowd watches others riding waves."
"yes"
[ { "entity": "others", "start_index": 18 }, { "entity": "waves", "start_index": 32 } ]
82
" The title screen returns and the man plays with the arrows above him again."
"yes"
[ { "entity": "the man", "start_index": 29 }, { "entity": "the arrows", "start_index": 48 } ]
83
" The fourth title screen and the man plays with arrows above."
"yes"
[ { "entity": "man", "start_index": 32 }, { "entity": "arrows", "start_index": 47 } ]
84
"A man puts on a helmet with a camera mounted on it."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "helmet", "start_index": 16 } ]
85
" The man makes a successful shot on goal and high fives a teammate."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "teammate", "start_index": 57 } ]
86
" A man stands the camel up on it's legs and walks around the area."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "camel", "start_index": 17 } ]
87
"A person is seen in various shots riding around in a canoe and smiling off into the distance."
"yes"
[ { "entity": "person", "start_index": 2 }, { "entity": "canoe", "start_index": 53 } ]
88
" The man continues moving along the water moving his paddle around while the camera captures his movements."
"yes"
[ { "entity": "man", "start_index": 4 }, { "entity": "water", "start_index": 35 }, { "entity": "paddle", "start_index": 52 } ]
89
"A group of men are riding around on horses while carry large sticks and holding them into the air."
"yes"
[ { "entity": "men", "start_index": 11 }, { "entity": "sticks", "start_index": 61 } ]
90
" He moves his legs up and his arms continuously following until he finishes the performance."
"yes"
[ { "entity": "He", "start_index": 0 }, { "entity": "legs", "start_index": 13 }, { "entity": "arms", "start_index": 29 } ]
91
" The camera zoomed in to the drums as the person continue to play."
"yes"
[ { "entity": "person", "start_index": 41 }, { "entity": "camera", "start_index": 4 } ]
92
"A person throw a disc with a stick, the disc arrives to a triangle on the floor where two people stand."
"yes"
[ { "entity": "person", "start_index": 2 }, { "entity": "disc", "start_index": 17 }, { "entity": "stick", "start_index": 29 } ]
93
" After, the person throw another disc that stops inside the triangle."
"yes"
[ { "entity": "person", "start_index": 11 }, { "entity": "disc", "start_index": 32 } ]
94
"Paintballs are flying at men in front of a tent."
"yes"
[ { "entity": "men", "start_index": 25 }, { "entity": "Paintballs", "start_index": 0 } ]
95
"An intro leads into several clips of a person lifting heavy weights over their shoulders as well as their arms all around."
"yes"
[ { "entity": "person", "start_index": 39 }, { "entity": "weights", "start_index": 60 } ]
96
" He is joined by a woman on a bass drum."
"yes"
[ { "entity": "He", "start_index": 0 }, { "entity": "woman", "start_index": 18 } ]
97
" The matador is being hit by the bull, while other matadors try to distract the bull."
"yes"
[ { "entity": "bull", "start_index": 32 }, { "entity": "matadors", "start_index": 50 } ]
98
"A woman blindfolded in a yard and swinging away at a pinata being moved around."
"yes"
[ { "entity": "woman", "start_index": 2 }, { "entity": "pinata", "start_index": 53 } ]
99
"A man is standing outside by the sidewalk cutting down a set of hedges."
"yes"
[ { "entity": "man", "start_index": 2 }, { "entity": "hedges", "start_index": 64 } ]
End of preview (truncated to 100 rows)

Dataset Card for LiMiT

Dataset Summary

Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. Literal-Motion-in-Text (LiMiT) dataset, is a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion.

Supported Tasks and Leaderboards

[More Information Needed]

Languages

The text in the dataset is in English (en).

Dataset Structure

Data Instances

Example of one instance in the dataset

{
    "id": 0,
    "motion": "yes",
    "motion_entities": [
        {
            "entity": "little boy",
            "start_index": 2
        },
        {
            "entity": "ball",
            "start_index": 30
        }
    ],
    "sentence": " A little boy holding a yellow ball walks by."
}

Data Fields

  • id: intger index of the example
  • motion: indicates whether the sentence is literal motion i.e. describes the movement of a physical entity or not
  • motion_entities: A list of dicts with following keys
    • entity: the extracted entity in motion
    • start_index: index in the sentence for the first char of the entity text

Data Splits

The dataset is split into a train, and test split with the following sizes:

train validation
Number of examples 23559 1000

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

[More Information Needed]

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

[More Information Needed]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{manotas-etal-2020-limit,
    title = "{L}i{M}i{T}: The Literal Motion in Text Dataset",
    author = "Manotas, Irene  and
      Vo, Ngoc Phuoc An  and
      Sheinin, Vadim",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.88",
    doi = "10.18653/v1/2020.findings-emnlp.88",
    pages = "991--1000",
    abstract = "Motion recognition is one of the basic cognitive capabilities of many life forms, yet identifying motion of physical entities in natural language have not been explored extensively and empirically. We present the Literal-Motion-in-Text (LiMiT) dataset, a large human-annotated collection of English text sentences describing physical occurrence of motion, with annotated physical entities in motion. We describe the annotation process for the dataset, analyze its scale and diversity, and report results of several baseline models. We also present future research directions and applications of the LiMiT dataset and share it publicly as a new resource for the research community.",
}

Contributions

Thanks to @patil-suraj for adding this dataset.