jason baldridge, associate professor of computational linguistics, university of texas at austin at...

Disambiguating Explicit and Implicit Geographic References

in Natural LanguageJason Baldridge @jasonbaldridge

Computational Linguistics LabDepartment of Linguistics

UT Austin

MLConf Seattle May 20, 2016

© 2016 Jason M Baldridge MLConf, May 2016

What does “barbecue” mean?

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What does “barbecue” mean? Barbecue’

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What I thought semantics was before 2005

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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

http://www.jstor.org/stable/10.1086/658894


Updated perspective a la Ray Mooney (UT Austin CS)

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

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

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http://www.lib.utexas.edu/books/travel/index.html


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

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Grapes of Wrath in Google Earth

Text

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

http://www.googlelittrips.com

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


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

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

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Topics with a clear, circumscribed geographic focus emerge!


Metadata is now plentiful

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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’’ W9

Geotagged Twitter


Geotagged Wikipedia

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30° 17′ N 97° 44′ W


Where’s a word on Earth? (according to Wikipedia)


Where’s a word on Earth? (according to Wikipedia)

mountain


Document geolocation: where is this person?

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

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

Language modeling approach

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

http://aclweb.org/anthology-new/P/P11/P11-1096.pdf


Locations of Twitter users are not uniformly distributed!

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(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

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

http://aclweb.org/anthology-new/D/D12/D12-1137.pdf

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

Automatic document geolocation


Hierarchical geo-location with logistic regression

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

© 2016 Jason M Baldridge MLConf May 2016

Hierarchical logistic regression

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Performance: hierarchical logistic regression with kd-tree grid

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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 510 km of truthPercent of documents within 166km (100 miles): 31%

Twitter (USA)Half of users geotagged within 192 km of truthPercent of documents within 166km (100 miles): 48%


Hierarchical logistic regression beats flat naive Bayes

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Naive Bayes Hierarchical LR

Twitter USA 36.2 48.0

Twitter World 28.7 31.3

Flickr 58.5 66

English Wikipedia 84.5 88.9

German Wikipedia 89.3 90.2

Portuguese Wikipedia 77.1 89.5

Accuracy @ 161 km, kd-tree grid


Logistic regression weights good features heavily

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Automated Lit Trips

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Toponym (place name) resolution

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They visit Portland every year.


Toponym (place name) resolution

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They visit Portland every year.

?

?

?

?

?

?

?

?

?

?

?

?

?

?

??

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


Toponym resolution in context

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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

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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.


Geo

Nam

es

4048392 Portland Mills Portland Mills 39.7781 -87.00918 P PPL US IN 133 0 223 218 America/Indiana/Indianapolis 2010-02-15 4084605 Portland Portland 32.15459 -87.1686 P PPL US AL 047 0 30 41 America/Chicago 2006-01-15 4127143 Portland Portland Portlend,Портленд 33.2379 -91.51151 P PPL US AR 003 430 38 39 America/Chicago 2011-05-14 4169227 Portland Portland 30.51242 -86.19578 P PPL US FL 131 0 8 14 America/Chicago 2006-01-15 4217115 Portland Portland 34.05732 -85.03634 P PPL US GA 233 0 229 228 America/New_York 2010-09-05 4277586 Portland Portland 37.0778 -97.31227 P PPL US KS 191 0 362 364 America/Chicago 2006-01-15 4305000 Portland Portland 37.12062 -85.44608 P PPL US KY 001 0 220 223 America/Chicago 2006-01-15 4305001 Portland Portland 38.26924 -85.8108 P PPL US KY 111 0 135 138 America/Kentucky/Louisville 2006-01-15 4305002 Portland Portland 38.74812 -84.44772 P PPL US KY 191 0 265 266 America/New_York 2006-01-15 404289 Portland Portland Portlend,Портленд 38.71088 -91.71767 P PPL US MO 027 0 170 172 America/Chicago 2010-01-29 4521811 Portland Portland Portlend,Портленд 39.00341 -81.77124 P PPL US OH 105 0 187 188 America/New_York 2010-01-29 4650946 Portland Portland Portlend,Портленд 36.58171 -86.51638 P PPL US TN 165 11480 244 245 America/Chicago 2011-05-14 4720131 Portland Portland Portlend,Портленд 27.87725 -97.32388 P PPL US TX 409 15099 13 11 America/Chicago 2011-05-14 4841001 Portland Portland Portlend,Портленд 41.57288 -72.64065 P PPL US CT 007 5862 24 27 America/New_York 2011-05-14 4871855 Portland Portland 43.12858 -93.12354 P PPL US IA 033 35 327 330 America/Chicago 2011-05-14 4906524 Portland Portland 41.66253 -89.98012 P PPL US IL 195 0 190 190 America/Chicago 2006-01-15 5006314 Portland Portland Portlend,Портленд 42.8692 -84.90305 P PPL US MI 067 3883 221 223 America/Detroit 2011-05-14 5746545 Portland Portland 45.52345 -122.67621 P PPLA2 US OR 051 583776 12 15 America/Los_Angeles 2011-05-14

Spatial minimality

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Geo

Nam

es


Spatial minimality

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Ann Arbor Detroit

Ionia Lyons

Portland White Pigeon

1 >7 >4

>15 >17

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# LocationsToponym





Geo

Nam

es


Spatial minimality

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PortlandLyonsIonia

White Pigeon

Ann Arbor Detroit

Ionia Lyons

Portland White Pigeon

1 >7 >4

>15 >17

1

# LocationsToponym





Spatial minimality often fails

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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”.

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


Spatial minimality often fails

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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!


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


Toponym classifiers

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


Toponym classifiers

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

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


Geographic word profiles learned from Wikipedia grid LMs

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TopoCluster: gazetteer-free toponym resolution

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Results: disambiguating toponyms

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TR-CoNLL LGL CWar WoTR

Population 91 63 62 64

SPIDER(spatial minimality)

65 68 67 69

WISTR(Wiki supervised)

89 64 73 63

SPIDER+WISTR

87 78 87 69

TopoCluster 92 75 93 71


New corpus: War of the Rebellion

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Historical document collection.

Both toponym and document lat/lon.

10,380 annotated toponyms, including lat/lon and regions.

Will be freely available.

Coming soon!

Contact me for details.


Grounding, more generally

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Grounding often involves connecting text to knowledge sources and other modalities such as demographics, time, image, and video.


Grounding, more generally

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Grounding often involves connecting text to knowledge sources and other modalities such as demographics, time, image, and video.

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

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

http://www.tweetolife.com/gender/


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

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

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

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slavetrenches aircraft

war


Temporal resolution [Kumar, Lease, and Baldridge 2011]

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2000

BC

0 A

D

2000

AD

4000

BC


More modalities: videos [Motwani & Mooney, 2012]

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Beyond word co-occurences for vector-space models

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bear boat car cow hadoop snow water wrench

3 234 42 4 1 2 325 0beach


Beyond word co-occurences for vector-space models

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bear boat car cow hadoop snow water wrench

3 234 42 4 1 2 325 0

beach


Combining distributional models with logics

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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

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beachchildren

visit

the children visit the beach

Agent Patient

This research was sponsored by:

Grant 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!

Thanks!

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

Publications - Benjamin Wing and Jason Baldridge. 2011. Simple supervised document geolocation with geodesic grids. In Proceedings of ACL HLT 2011.

- 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 2014. Hierarchical Discriminative Classification for Text-Based Geolocation. EMNLP 2014.

Document geolocation

https://github.com/utcompling/textgrounder

http://aclweb.org/anthology-new/P/P11/P11-1096.pdf

http://aclweb.org/anthology-new/D/D12/D12-1137.pdf

Code and data - Fieldspring: https://github.com/utcompling/fieldspring- TopoCluster: https://github.com/grantdelozier/TopoCluster

Publications - Mike Speriosu and Jason Baldridge. 2013. Text-Driven Toponym Resolution using Indirect Supervision. ACL 2013.

- Grant DeLozier, Jason Baldridge, and Loretta London. 2015. Gazetteer-Free Toponym Resolution Using Geographic Word Profiles. AAAI 2015.

Toponym resolution

https://github.com/utcompling/fieldspring

http://www.jasonbaldridge.com/papers/speriosu-baldridge-acl2013.pdf

jason baldridge, associate professor of computational linguistics, university of texas at austin at...

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