lecture: semantic word clouds

Seman&c Analysis in Language Technology http://stp.lingfil.uu.se/~santinim/sais/2014/sais_2014.htm

Semantic Word Clouds

Marina San(ni [email protected]

Department of Linguis(cs and Philology Uppsala University, Uppsala, Sweden

Autumn 2014

1 Lect 10: Seman(c Word Clouds

Acknowledgements

•  Some slides borrowed from Sergey Pupyrev.

Lect 10: Seman(c Word Clouds 2

Outline

•  Word Clouds •  3 early algorithms •  3 new algorithms •  Metrics & Quan(ta(ve Evalua(on


Word Clouds

•  Word clouds have become a standard tool for abstrac(ng, visualizing and comparing texts…

•  We could apply the same or similar techniques to the huge amonts of tags produced by users interac(ng in the social networks


Comparison & conceptualiza(on Tool


•  Word Clouds as a tool for ”conceptualizing” documents. Cf Ontologies

•  Ex: 2008, comparison of speeches: Obama vs McCain

Word Clouds and Tag Clouds…

•  … are oVen used to represent importance among terms (ex, band popularity) or serve as a naviga(on tool (ex, Google search results).


The Problem…

•  How to compute seman(c-‐preserving word clouds in which seman(cally-‐related words are close to each other.


Wordle h^p://www.wordle.net

•  Prac(cal tools, like Wordle, make word cloud visualiza(on easy.

•  Shortoming: they do not capture the rela(onships between words in any way


Many word clouds are arranged randomly (look also at the sca^ered colours)


Seman(c Pa^erns

•  Humans ins(nc(vely tend to pick up pa^erns

•  Ins(nc(vely, one could say that two words that are close to each other in a word cloud are seman(cally related.


So, it makes sense to place such related words close to each other (look also at the color distribu(on)


In linguis(cs and in LT…

•  … if a pair of words oVen appear together in a sentence, then we can assume that this pair of words is related seman(cally.


Seman(c word clouds have higher user sa(sfac(on compared to other layouts…


All recent word cloud visualiza(on tools aim to incoprorate seman(cs in the layout…


… but none of them provide any guarantee about the quality of the layout in terms of seman(cs


Early algorithms: Force-‐Directed Graph

•  Most of the exis(ng algorithms are based on force-‐directed graph layout.

•  Force-‐directed graph drawing algorithms are a class of algorithms for drawing graphs in an aesthe(cally pleasing way

–  A^rac(ve forces between pairs to reduce empty space

–  Repulsive forces ensure that words do not overlap

–  Final force preserve seman(c rela(ons between words.


Force-‐directed graph drawing algorithms assign forces among the set of edges and the set of nodes of a graph drawing. Typically, spring-‐like a^rac(ve forces based on Hooke's law are used to a^ract pairs of endpoints of the graph's edges towards each other, while simultaneously repulsive forces like those of electrically charged par(cles based on Coulomb's law are used to separate all pairs of nodes.

Newer Algorithms: rectangle representa(on of graphs

•  Vertex-‐weighted and edge-‐weighed graph: –  The ver(ces of the graph are the words

•  Their weight correspond to some measure of importance (eg. word frequencies)

–  The edges capture the seman(c relatedness of pair of words (eg. co-‐occurrence) •  Their weight correspond to the strength of the rela(on

–  Each vertex can be drawn as a box (rectangle) with a dimension determing by its weight

– A realized adjacency is the sum of the edge weights for all pairs of touching boxes.

–  The goal is to maximize the realized adjacencies.


Experimental Setup: 1) Term Extrac(on 2) Ranking 3) Similarity Conputa(on


Early Algorithms

1.  Wordle (Random) 2.  Context-‐Preserving Word Cloud Visualiza(on

(CPWCV) 3.  Seam Carving


Wordle à Random

•  The Wordle algorithm places one word at a (me in a greedy fashion, aiming to use space as efficiently as possible.

•  First the words are sorted by weight in decreasing order.

•  Then for each word in the order, a posi(on is picked at random.


1: Random


2: Random


3: Random


4: Random


5: Random


6: Random


Context-‐Preserving Word Cloud Visualiza(on (CPWCV)

•  First, a dissimilarity matrix is computed and Mul(dimensional Scaling (MDS) is performed

•  Second, effort to create a compact layout


Mul(dimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset.

1: Context-‐Preserving


2: Context-‐Preserving : repulsive force


3: Context-‐Preserving : a^rac(ve force


Seam Carving

•  Seam carving is a content-‐aware image resizing technique

•  Basically, an algorithm for image resizing

•  It was invented at Mitsubishi’s


1: Seam Carving


2: Seam Carving : space is divided into regions


3: Seam Carving : empty paths trimmed out itera(vely


4: Seam Carving


5: Seam Carving


6: Seam Carving: space divided into regions


7: Seam Carving


3 New Algorithms

1.  Inflate and Push 2.  Star Forest 3.  Cycle Cover


Inflate-‐and-‐Push

•  Simple heuris(c method for word layout, which aims to preserve seman(c rela(ons between pair of words.


1: Inflate


2: Inflate : scaling down


3: Inflate : seman(cally-‐related words are placed close to each other


4: Inflate : repulsive force to resolve overlaps


5: Inflate


Star Forest

•  A star is a tree and a star forest is a forest whose connected components are all stars.


Star Forest : star = graph •  Dissimilarity matrix à disjoint stars = star forest •  A^rac(ve force to get a compact layout


Cycle Cover •  This algorithm is based on a similarity matrix. •  First, a similarity path(=cycle) is created •  Then, the op(mal level of compact-‐ness is computed


Quan(ta(ve Metrics


Criteria 1.  Realized Adjacenies –  how close are similar words to each other?

2.  Distor(on –  how distant are dissimilar words?

3.  Comptactness –  how well u(lized is the drawing area?

4.  Uniform Area U(liza(on –  uniformity of the distribu(on (overpopulated vs sparse areas

in the word cloud) 5.  Aspect Ra(o –  width and height of the bounding box

6.  Running Time –  execu(on (me


2 datasets

(1) WIKI , a set of 112 plain-‐text ar(cles extracted from the English Wikipedia, each consis(ng of at least 200 dis(nct words (2) PAPERS , a set of 56 research papers published in conferences on experimental algorithms (SEA and ALENEX) in 2011-‐2012.


Cycle Cover wins


Seam Carving wins


Random wins


Inflate wins


Random and Seam Carving win


All ok except Seam Carving


Demo


Final Words


The end

60 Lect 10: Seman(c Word Clouds

lecture: semantic word clouds

Education

word clouds word clouds

outline word clouds

tag clouds

semanc pa

obama vs mccain word

semanc analysis

semanc relaons

layout lect