grounding text

Grounding Text

Jason Baldridge @jasonbaldridge

Austin Data 2014

Associate Professor Co-founder & Chief Scientist

Friday, September 5, 14

What does “barbecue” mean?

What does “barbecue” mean? Barbecue’

What I thought semantics was before 2005

From: John Enrico and Jason Baldridge. 2011. Possessor Raising, Demonstrative Raising, Quantifier Float and Number Float in Haida. International Journal of American Linguistics. 77(2):185-218

Updated perspective a la Ray Mooney (UT Austin CS)

http://www.cs.utexas.edu/users/ml/slides/chen-icml08.ppt

http://www.lib.utexas.edu/books/travel/index.htmlTravel at the Turn of the 20th Century

Motivation: Google Lit Trips [http://www.googlelittrips.com/]

Grapes of Wrath in Google Earth

http://www.googlelittrips.com/GoogleLit/9-12/Entries/2006/11/1_The_Grapes_of_Wrath_by_John_Steinbeck.html

Motivation: Google Lit Trips [http://www.googlelittrips.com/]

Grapes of Wrath in Google Earth

http://www.googlelittrips.com/GoogleLit/9-12/Entries/2006/11/1_The_Grapes_of_Wrath_by_John_Steinbeck.html

Crisis response: Haiti earthquake

Look, Mom, no hands! (Err, um... no metadata.)

Topics with a clear, circumscribed geographic focus emerge!

But, of course, metadata is now plentiful.

Geotagged Wikipedia

30° 17′ N 97° 44′ W

01:55:55 RT @USER_dc5e5498: Drop and give me 50....

05:09:29 I said u got a swisher from redmond!? He said nah kirkland! Lol..ooooooooOkay!

05:57:35 Lmao!:) havin a good ol time after work! Unexpected! #goodtimes

06:00:09 RT @USER_d5d93fec: #letsbereal .. No seriously, #letsbereal>>lol. Don't start.

06:00:37 On my way to get @USER_60939380 yeee! She want some of this strawberry! Sexy!

47°31’41’’ N 122°11’52’’ W11

Geotagged Twitter

01:55:55 RT @USER_dc5e5498: Drop and give me 50....

05:09:29 I said u got a swisher from redmond!? He said nah kirkland! Lol..ooooooooOkay!

05:57:35 Lmao!:) havin a good ol time after work! Unexpected! #goodtimes

06:00:09 RT @USER_d5d93fec: #letsbereal .. No seriously, #letsbereal>>lol. Don't start.

06:00:37 On my way to get @USER_60939380 yeee! She want some of this strawberry! Sexy!

47°31’41’’ N 122°11’52’’ W11

Geotagged Twitter

Document geolocation: where is this person?

Amsterdam, Zaandam, Amstelveen, Diemen, Landsmeer ...

Frankfurt, Frechen, Hürth, Brühl, Wesseling, ...

Language modeling approach

Wing & Baldridge 2011: Simple supervised document geolocation with geodesic grids.

Amsterdam, Zaandam, Amstelveen, Diemen, Landsmeer ...

Frankfurt, Frechen, Hürth, Brühl, Wesseling, ...

Language modeling approach

Wing & Baldridge 2011: Simple supervised document geolocation with geodesic grids.

mountainbeach

wine barbecue

Where’s a word on Earth?

mountainbeach

wine barbecue

Where’s a word on Earth?

Locations of Twitter users are not uniformly distributed!

(Small) GeoUT (Twitter) plotted on Google Earth, one pin per user.

Density of (all) documents in GeoUT

over the USA (390 million tweets)

k-d tree for geotagged Wikipedia, looking at N. America

Roller, Speriosu, Rallapalli, Wing & Baldridge 2014: Supervised Text-based Geolocation Using Language Models on an Adaptive Grid.

k-d tree for geotagged Wikipedia, looking at N. America

Roller, Speriosu, Rallapalli, Wing & Baldridge 2014: Supervised Text-based Geolocation Using Language Models on an Adaptive Grid.

Pre-grid clustering [Erik Skiles, MA thesis, UT Austin, Ling]

Four clusters on GeoUT (390 million tweets)

West coast East coast Midwest & South Spanish language

All tweets

[Serdyukov, Murdock, & van Zwol 2009; Cheng, Caverlee, & Lee 2010; Wing & Baldridge 2011]

Automatic document geolocation

[Serdyukov, Murdock, & van Zwol 2009; Cheng, Caverlee, & Lee 2010; Wing & Baldridge 2011]

Automatic document geolocation

Image geo-location: http://graphics.cs.cmu.edu/projects/im2gps/

Performance (kd-tree with clustering)

Wikipedia (entire world)Half of documents geotagged within 12 km of truthPercent of documents within 166km (100 miles): 91%

Twitter (USA)Half of users geotagged within 330 km of truthPercent of documents within 166km (100 miles): 40%

For better or worse, it soon might not matter whether you have location turned on or not... what

you say is where you are / are from. (Also, other factors, e.g. who you are linked to, of course.)

Hierarchical geo-location with logistic regression

Wing & Baldridge 2014: Hierarchical Discriminative Classification for Text-Based Geolocation.

Performance (kd-tree with clustering)

Flickr (entire world)Half of documents geotagged within 18 km of truthPercent of documents within 166km (100 miles): 66%

Twitter (World)Half of users geotagged within 490 km of truthPercent of documents within 166km (100 miles): 31%

Twitter (USA)Half of users geotagged within 170 km of truthPercent of documents within 166km (100 miles): 49%

Hierarchical logistic regression beats flat naive Bayes

Naive Bayes Hierarchical LR

Twitter USA

Twitter World

Flickr

English Wikipedia

German Wikipedia

Portuguese Wikipedia

36.2 49.2

28.7 31.3

58.5 66.0

84.5 88.9

89.3 90.2

77.1 89.5

Accuracy @ 161 km, kd-tree grid

Logistic regression weights good features heavily

Toponym (place name) resolution

They visit Portland every year.

Toponym (place name) resolution

They visit Portland every year.

Which Portland? (Also: Canada, Australia, Ireland...)

Toponym resolution in context

Although Elisha Newman made the first land entry in the township of Portland (June, 1833), he did not become a settler until three years later, by which time a few settlers had located in the town. From Mr. Newman's story, it appears that early in 1833, he was visiting friends in Ann Arbor, and during an evening conversation discussed with others the subject of unlocated lands lying west of Ann Arbor. One of the company (Joseph Wood) remarked that he had been out with the party sent to survey Ionia and other counties, and that the surveyors were struck by the valuable water-power at the mouth of the Looking Glass River, saying there would surely be a village there some day.Mr. Newman was at once taken with the idea of locating lands at the mouth of the Looking Glass. Following up his impulse, he made ready to start at once, and, accompanied by James Newman and Joseph Wood, went out to the Looking Glass on a tour of inspection. Being satisfied with the location, he returned Eastward with his companions, and at White Pigeon made his land entry.Newman did not return for a permanent settlement until the spring of 1836, and meanwhile, in November, 1833, Philo Bogue bought a piece of land on section 28, in the bend of the Grand River, where he proposed to set up a trading post. Unaided he rolled up a log cabin near where the Detroit, Lansing, and Northern depot was located, and when he brought the house into decent shape went over to Hunt's at Lyons for his family, whom he had left there against such time as he should have affairs prepared for their comfort.

Spatial minimality

Although Elisha Newman made the first land entry in the township of Portland (June, 1833), he did not become a settler until three years later, by which time a few settlers had located in the town. From Mr. Newman's story, it appears that early in 1833, he was visiting friends in Ann Arbor, and during an evening conversation discussed with others the subject of unlocated lands lying west of Ann Arbor. One of the company (Joseph Wood) remarked that he had been out with the party sent to survey Ionia and other counties, and that the surveyors were struck by the valuable water-power at the mouth of the Looking Glass River, saying there would surely be a village there some day.

Mr. Newman was at once taken with the idea of locating lands at the mouth of the Looking Glass. Following up his impulse, he made ready to start at once, and, accompanied by James Newman and Joseph Wood, went out to the Looking Glass on a tour of inspection. Being satisfied with the location, he returned Eastward with his companions, and at White Pigeon made his land entry.

Newman did not return for a permanent settlement until the spring of 1836, and meanwhile, in November, 1833, Philo Bogue bought a piece of land on section 28, in the bend of the Grand River, where he proposed to set up a trading post. Unaided he rolled up a log cabin near where the Detroit, Lansing, and Northern depot was located, and when he brought the house into decent shape went over to Hunt's at Lyons for his family, whom he had left there against such time as he should have affairs prepared for their comfort.

4048392 Portland Mills Portland Mills 39.7781 -87.00918 P PPL US IN 133 0 223 218 America/Indiana/Indianapolis 2010-02-154084605 Portland Portland 32.15459 -87.1686 P PPL US AL 047 0 30 41 America/Chicago 2006-01-154127143 Portland Portland Portlend,Портленд 33.2379 -91.51151 P PPL US AR 003 430 38 39 America/Chicago 2011-05-144169227 Portland Portland 30.51242 -86.19578 P PPL US FL 131 0 8 14 America/Chicago 2006-01-154217115 Portland Portland 34.05732 -85.03634 P PPL US GA 233 0 229 228 America/New_York 2010-09-054277586 Portland Portland 37.0778 -97.31227 P PPL US KS 191 0 362 364 America/Chicago 2006-01-154305000 Portland Portland 37.12062 -85.44608 P PPL US KY 001 0 220 223 America/Chicago 2006-01-154305001 Portland Portland 38.26924 -85.8108 P PPL US KY 111 0 135 138 America/Kentucky/Louisville 2006-01-154305002 Portland Portland 38.74812 -84.44772 P PPL US KY 191 0 265 266 America/New_York 2006-01-15404289 Portland Portland Portlend,Портленд 38.71088 -91.71767 P PPL US MO 027 0 170 172 America/Chicago 2010-01-294521811 Portland Portland Portlend,Портленд 39.00341 -81.77124 P PPL US OH 105 0 187 188 America/New_York 2010-01-294650946 Portland Portland Portlend,Портленд 36.58171 -86.51638 P PPL US TN 165 11480 244 245 America/Chicago 2011-05-144720131 Portland Portland Portlend,Портленд 27.87725 -97.32388 P PPL US TX 409 15099 13 11 America/Chicago 2011-05-144841001 Portland Portland Portlend,Портленд 41.57288 -72.64065 P PPL US CT 007 5862 24 27 America/New_York 2011-05-144871855 Portland Portland 43.12858 -93.12354 P PPL US IA 033 35 327 330 America/Chicago 2011-05-144906524 Portland Portland 41.66253 -89.98012 P PPL US IL 195 0 190 190 America/Chicago 2006-01-155006314 Portland Portland Portlend,Портленд 42.8692 -84.90305 P PPL US MI 067 3883 221 223 America/Detroit 2011-05-145746545 Portland Portland 45.52345 -122.67621 P PPLA2 US OR 051 583776 12 15 America/Los_Angeles 2011-05-14

Spatial minimality

Ann ArborDetroit

IoniaLyons

PortlandWhite Pigeon

>15>17

# LocationsToponym

Spatial minimality

PortlandLyonsIonia

White Pigeon

Ann ArborDetroit

IoniaLyons

PortlandWhite Pigeon

>15>17

# LocationsToponym

Spatial minimality often fails

I moved from Encinitas, CA, a nice beach town in North San Diego County to Asheville, NC. By far, Ashville is more hip, especially West Asheville. Asheville has a lot in common with Portland. Austin, I've never been to so I cannot comment. But what makes a place cool and hip, in my opinion are that give a area "punch". There are a lot of ingredients. One is geography. Add a college or university (and all that they bring- and draw), good restaurants, a good music scene, a progressive attitude and tolerance. Hmmm. I'm sure there are many more to ponder. But that's my start. Oh, lots of bars!

From: http://www.city-data.com/forum/austin/1694181-what-makes-city-like-austin-portland-3.html

City-data.com incorrectly marks “West” and “Portland” as the cities in Texas -- presumably because of their textual and spatial proximity to “Austin”.

Spatial minimality often fails

From: http://www.city-data.com/forum/austin/1694181-what-makes-city-like-austin-portland-3.html

City-data.com incorrectly marks “West” and “Portland” as the cities in Texas -- presumably because of their textual and spatial proximity to “Austin”.

But: it is clear from the text that Portland, Oregon and Austin, Texas are the referents, though their states are never mentioned and are far from the other locations!

Toponym classifiers

Strategy: build a textual classifier per toponym by obtaining indirectly labeled examples from Wikipedia.

Toponym classifiers

P(Portland-OR|music) > P(Portland-ME|music)P(Portland-OR|wharf ) < P(Portland-ME|wharf )

Results: disambiguating toponyms

Average error distance

Accuracy Average error distance

Accuracy

Population

SPIDER(spatial minimality)

WISTR(Wiki supervised)

SPIDER+WISTR

216 81.0 1749 59.7

2180 30.9 266 57.5

279 82.3 855 69.1

430 81.8 201 85.9

TR-CoNLLReuters News Texts

August 1996

Perseus Civil War CorpusBooks

Late 19th Century

Take-home message: text classifiers are very effective & can be boosted by spatial minimality algorithms.

Identifying, disambiguating, and displaying toponyms

Back to grounding

Grounding often involves connecting text to knowledge sources and other modalities (image, video) & bootstrapping.

Back to grounding

Grounding often involves connecting text to knowledge sources and other modalities (image, video) & bootstrapping.

Also, they can help us create models for deeper aspects of language, such as syntactic structure and logical form.

Lexical brain decoding [Yarkoni, Poldrack, Nichols, Van Essen & Wager (2011)]

He says, she says http://www.tweetolife.com/gender/

Temporality of words, by hour http://www.tweetolife.com/hour/

Temporality of expressions, by day: http://www.google.com/trends

Temporality of expressions, by year: http://ngrams.googlelabs.com/

slavetrenches aircraft

Temporal resolution [Kumar, Lease, and Baldridge 2011]

More modalities: videos [Motwani & Mooney, 2012]

Beyond word co-occurences for vector-space models

bear boat car cow hadoop snow water wrench

3 234 42 4 1 2 325 0beach

3 234 42 4 1 2 325 0

Combining distributional models with logics

Erk (2013): “Towards a semantics for distributional representations.”

Garrette et al (2012): “A formal approach to linking logical form and vector-space lexical semantics”Beltagy et al (2013): “Montague Meets Markov: Deep Semantics with Probabilistic Logical Form”

Multi-component structured vector-space models

beachchildren

the children visit the beach

Agent Patient

Language learning in context [Kim & Mooney, 2013]

All your meaning are belong to us

http://davidrothman.net/2009/09/02/all-your-healthbase-are-belong-to-us-want-em-back/

Grounding matters

Junto - label propagationhttps://github.com/scalanlp/junto

Textgrounder - document geolocationhttps://github.com/utcompling/textgrounder

Fieldspring - toponym resolutionhttps://github.com/utcompling/fieldspring

Low-resource POS tagginghttps://github.com/dhgarrette/low-resource-

pos-tagging-2013

Updown - polarity classificationhttps://github.com/scalanlp/junto

OpenNLP - machine learning / NLPhttp://opennlp.apache.org/

Open Source Software (Scala/Java)

pos-tagging-2013

Nak - machine learninghttps://github.com/scalanlp/nak

Chalk - NLPhttps://github.com/scalanlp/chalk

Breeze - linear algebrahttps://github.com/scalanlp/nak

pos-tagging-2013

Nak - machine learninghttps://github.com/scalanlp/nak

Chalk - NLPhttps://github.com/scalanlp/chalk

Breeze - linear algebrahttps://github.com/scalanlp/nak

This research was sponsored by:

Grant: W911NF-10-1-0533Grant from the Morris Memorial Trust Fund

- Walt Whitman, A Song of the Rolling Earth (in Leaves of Grass)

Final note: Whitman had it right many years ago!

Supervision- documents labeled with latitude & longitude

Methods- Language Modeling for Information Retrieval

Code- Textgrounder: https://github.com/utcompling/textgrounder

Publications- Stephen Roller, Mike Speriosu, Sarat Rallapalli, Benjamin Wing and Jason Baldridge. 2012. Supervised Text-based Geolocation Using Language Models on an Adaptive Grid. EMNLP 2012. Jeju, Korea.- Benjamin Wing and Jason Baldridge. 2011. Simple supervised document geolocation with geodesic grids. In Proceedings of ACL HLT 2011.

Document geolocation

Supervision- indirectly acquired toponym annotations using a gazeteer and geo-annotated Wikipedia

Methods- logistic regression- named entity recognition

Code- Fieldspring: https://github.com/utcompling/fieldspring

Publications- Mike Speriosu and Jason Baldridge. Text-Driven Toponym Resolution using Indirect Supervision. To appear in proceedings of ACL 2013.

Toponym resolution

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