mw2011: klavans, j. +, computational linguistics in museums: applications for cultural datasets

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Klavans

Judit

hRobert Stein

SusanChun

Raul Guerr

a

Computational Linguistics in Museums:

Applications for Cultural Datasets

COMPUTATIONALLINGUISTICS

• Language - Words, Words, Words• Use• Meaning• Syntax• Shape of words• Sounds

APPLICATIONS

• Speech synthesis – 1980’s Talking Machines for the Blind

• Intelligent search – pre-google• Finding names – who, what, where• Translation• Speech recognition• Answering Questions – What is

Watson?

DOMAINS FOR COMPUTATIONAL

LINGUISTICS• Healthcare – interpreting patient records• Government – helping people find

information• International Affairs – cross-language

translation• Law – analyzing Enron scandal email• Marketing – Opinions on products• Museums – analyzing text and tags

associated with objects for better access

Computational Linguistics forMetadataBuilding+

Klavans

Judit

hRobert Stein

SusanChun

Raul Guerr

a

Computational Linguistics in Museums:

Applications for Cultural Datasets

INTERDISCIPLINARYRESEARCH

Computational Linguistics

in Museums

Text, Tags, Trust

• Funded in 2008 by IMLS• With the University of Maryland, and

collaborative of museum partners• Studying the relationships between

social tags, scholarly text and resources, and the application of trust networks to improve access to museum collections.

MW 2011 Contributions

• Which Computational Linguistic tools can or should be applied to tags?

• How do these tools impact tag analysis?

• What results differ from the initial steve.museum results from Trant 2007?

• So what – for CL?

• So what – for Museums?

Hard Challenges

• What do these words really mean?• How can tags be related to other tags?

across languagesacross users

• How are tags over museum objects related to tags over anything else?• How can they be used?

FINDING A NEEDLE IN THE HAYSTACK

This canvas was the first one Gauguin painted during the two months he spent in Provence.... Gauguin had rebelled against Impressionism's reliance on the visible world, and he altered nature's shapes and colors to suggest his own more subjective reaction to the landscape.

While the rural subject and acidic colors show the influence of van Gogh, this image is more indebted to Paul Cézanne. In his careful integration of the haystack and farm buildings, Gauguin has echoed Cézanne's emphasis on geometric form.

Gallery Label

TOOLS FOR TAGS• Morphological Analysis – Conflate

when possible– Cats, cat– Haystacks, haystack– Painting, paint ?

• What words are verbs, nouns, adjectives?

• How should multi-word tags be handled?

Raw Tags or Tokens

Results

25%

68%

93%

1. NN=252052. JJ=63193. NNS=40414. NN_NN=22575. JJ_NN=17926. VBG=10437. VBN=7278. NP=7089. OD_NN=45410. JJ_NNS=413

Top 10 POS Patterns:1. NN=67062. NN_NN=17133. JJ_NN=11944. JJ=9215. NNS=7576. JJ_NNS=3037. NN_NNS=3008. VBG=2389. NP=20910. VBN_NN=202

Hard Challenges

• What do these words really mean?• How can tags be related to other tags?

across languagesacross users

• How are tags over museum objects related to tags over anything else?• How can they be used?

• Integral to most language processing pipelines• Irecursor to parsing.

• However, for social tags, parsing is not a meaningful step.

Research:• Understand the nature of this kind of descriptive tagging. • Link part of speech information with other lexical resources for disambiguation

Why Part of Speech?

Gold

Orange

Necklace

Ripe

You shall know a word by the company it keeps. J.R. Firth

WHAT ABOUT “NEW ENGLAND”

• Idioms / lexicalized phrases are more difficult

• Heuristic comparison to Wikipedia Titles matched 46% (30% distinct) of multiword tags

• E.g. “Grapes of Wrath”, “Irish Wolfhound”, “Franco-Prussian War”

*Klavans and Golbeck, 2010

WISH LIST - BETTER WAYS TO TAME THE

PROLIFERATION OF RICH BUT “NOISY” CONTENT

• Clustering over tags for similarity• Clustering over tags and terms from

text• Matching over existing terms to

identify meaningful units• Apply machine learning techniques

to guess meaning• Bigrams, Trigram, Thesauri, Corpus

Analysis

ACKNOWLEDGEMENTSSteve.museum project membersT3 and steve.museum museum partnersUniversity of Maryland, T3 groupIMA Museum ……and other participants

THANK YOU!

Questions?