Learning Prototypical Goal Activities for Locations Tianyu Jiang, - - PowerPoint PPT Presentation

learning prototypical goal activities for locations
SMART_READER_LITE
LIVE PREVIEW

Learning Prototypical Goal Activities for Locations Tianyu Jiang, - - PowerPoint PPT Presentation

Learning Prototypical Goal Activities for Locations Tianyu Jiang, Ellen Riloff University of Utah July 17, 2018 In Infer erenc ences es We e Are e Mak aking ing John went to the restaurant. What did he do? sat down


slide-1
SLIDE 1

Learning Prototypical Goal Activities for Locations

Tianyu Jiang, Ellen Riloff University of Utah July 17, 2018

slide-2
SLIDE 2

In Infer erenc ences es We e Are e Mak aking ing

2

John went to the restaurant. What did he do?

  • “sat down”
  • “read the menu”
  • “ate food”
  • “left a tip”
slide-3
SLIDE 3

Pr Proto totypical Activities

3

People go to __________ to __________ . We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). libraries study churches pray hospitals see a doctor, have surgery ACL learn others’ work, meet people, give talk

slide-4
SLIDE 4

Ho How w is is it it us useful? ul?

4

Conversational systems, question answering, semantic disambiguation... Notice different inferences of “got”!

(a) She went to the kitchen and got chicken. (b) She went to the supermarket and got chicken. retrieve purchase

I just went to the beach. Did you go swimming?

slide-5
SLIDE 5

Wha What is is a lo locatio tion?

5

  • Geographic Coordinate:

(37.81 S, 144.96 E)

  • Political Region:

China, Melbourne

  • Institution:

school, hospital

  • Landscape:

mountain, forest

  • Organization:

Walmart, Sunoco

slide-6
SLIDE 6

Ar Are these locations?

They act as locations in the phrase “go to X”. For example, “go to doctor” => “go to the doctor’s office”.

6

doctor Wikipedia bedroom

slide-7
SLIDE 7

7

(Loc, Act)

Fr Fram amework

Activity Profile Y Ranked list of Activities for Locations

Until Convergence Location Similarity W Activity Similarity A

Corpus Labeled Data

slide-8
SLIDE 8

Loc Location

  • n an

and Activity tivity Extr trac actio tion

Dataset: 2011 Spinn3r Weblog Subset (Burton et al., 2011) Pattern 1: go to X to Y extract (Loc, Act) pairs Pattern 2: Y in/at X extract more Acts

8

slide-9
SLIDE 9

Hig High h Fr Freq eq No Not Gu Guarantee “ “Go Goal”

9

  • (clinic, have appointment):

not goal

  • (university, study law):

too specific

  • (Disneyland, visit):

too general

slide-10
SLIDE 10

10

(Loc, Act)

Fr Fram amework

Activity Profile Y Ranked list of Activities for Locations

Until Convergence Location Similarity W Activity Similarity A

Corpus Labeled Data

slide-11
SLIDE 11

Ac Activity Profile

Activity profile matrix Y,

where Yi,j represents the strength of the j th activity aj being a goal-act for the i th location li.

11

a1=buy book a2=eat burger

...

am=pray l1=McDonald’s

5 300 1

l2=Burger King

2 500 2

l3=bookstore

400 20 4 ...

ln=church

5 10 700

An illustration of the activity profile matrix Y .

slide-12
SLIDE 12

Ac Activity Profile Learning

Intuitively, we assume that similar locations share similar activity profiles, which motivates the objective function over Y:

Initialization yi

0is a mix of co-occurrence data and labeled data.

12

slide-13
SLIDE 13

13

(Loc, Act)

Fr Fram amework

Activity Profile Y Ranked list of Activities for Locations

Until Convergence Location Similarity W Activity Similarity A

Corpus Labeled Data

slide-14
SLIDE 14

Ac Activity Similarity Matrix

14

co-occurrence word matching embedding

slide-15
SLIDE 15

Go Gold ld Stan andar ard Da Data

We use Amazon Mechanical Turk to ask workers to provide ONE primary activity that is the reason why a person would go to the listed locations. People go to LOC to ___ ___

VERB NOUN

We got answers for 200 locations from each of the 10 workers.

15

slide-16
SLIDE 16

Go Gold ld Stan andar ard Da Data

16

Location Gold Goal-Acts Toys R Us buy toys (9), browse gifts sink wash hands (7), wash dishes (3) airport catch flight (7), board planes, take airplane, take trips bookstore buy books (6), browse books (2), browse bestsellers, read book lake go fishing (3), go swimming (3), drive boat (2), ride boat, see scenery chiropractor get treatment (3), adjust backs (3), alleviate pain (2), get adjustment, get aligned Chinatown buy goods (2), buy duck, buy souvenirs, eat dim sum, eat rice, eat won- tons, find Chinese, speak Chinese, visit restaurants

Goal-acts provided by human annotators.

slide-17
SLIDE 17

Go Gold ld Stan andar ard Da Data

96% 78% 53% 39% 25% 15% 6% 2% 0.5% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2 3 4 5 6 7 8 9 10

% of Locations # of Annotators Listing the Same Activity

Percentage of locations that have at least one goal-act assigned by multiple annotators.

40%

At least half of annotators listed the same goal-act for nearly 40% of the locations.

1

Only 1 location was assigned exactly the same goal-act by all annotators. 17

slide-18
SLIDE 18

Ev Evaluation Metrics

Our systems produce a ranked list of hypothesized goal-acts for a location. Mean Reciprocal Rank(MRR) is used to judge the quality of the top 10 activities for each location.

18

slide-19
SLIDE 19

Expe Experimental Resul sults

19

MRRE MRRP EMBED

0.02 0.09

PMI

0.20 0.33

FREQ

0.23 0.34

AP

0.28 0.38

AP+AL

0.28 0.40

AP+AO

0.23 0.33

AP+AE

0.25 0.36

AP+AL+E

0.29 0.42

Scores for MRR.

slide-20
SLIDE 20

Expe Experimental Resul sults

20

TOP1 TOP2 TOP3 EMBED

0.05 0.08 0.12

PMI

0.25 0.36 0.41

FREQ

0.23 0.32 0.40

AP

0.29 0.41 0.47

AP+AL

0.32 0.44 0.49

AP+AO

0.24 0.35 0.43

AP+AE

0.28 0.40 0.47

AP+AL+E

0.35 0.44 0.52

Scores for Top K results.

slide-21
SLIDE 21

Expe Experimental Resul sults

21

Location Gold Activity List AP + AL+E Top 3 PMI Top 3

bookstore buy book (6) browse book (2) browse bestseller read book buy book purchase book see book buy copy purchase book buy book pharmacy get drug (4) fill prescription (3) get prescription (2) buy medicine find medicine get prescription pick up prescription buy pill fill prescription pick up prescription university get degree (4) gain education (5) watch sport gain education further education gain knowledge study law study psychology pursue study Meijer buy grocery (8) buy cream

  • btain grocery

buy item go shopping get item check out deal have shopping post today Examples of Top 3 hypothesized prototypical goal activities.

slide-22
SLIDE 22

Expe Experimental Resul sults

22 Examples of Top 3 hypothesized prototypical goal activities.

Location Gold Activity List AP + AL+E Top 3 PMI Top 3

market buy grocery (6) buy fresh, buy goods buy shirt, find produce make money eat out eat lunch have demand increase competition lead player phone make call (4), NOT LOC (2) answer call, call friend have conversation stop ring play game browse website view website put number have number put card

slide-23
SLIDE 23

Con Conclusion

  • ns
  • We introduced the problem of learning prototypical goal activities for

locations.

  • Human annotations showed that people do associate prototypical

goal-acts with locations.

  • Future:
  • More data collection.
  • Take advantage of more contextual information and other external

knowledge.

23

slide-24
SLIDE 24

Th Than ank yo you! Qu Ques estions?

24