Survey Analysis

An attempt to develop an Intuition of Semantic Relatedness

Outline

• Motivation• Survey framework• Analysis

Motivation• Semantic Relatedness – broad/subjective concept• Given a pair of words –

• Are they related?• If so, to what extent?• What is the kind of relationship between them?

• Answer varies from person to person – depends on his background, culture, work domain etc.

• Example: Apple - Computer

Existing Datasets

• Rubenstein & Goodenough (1965) – 65 English noun pairs (RG - 65)

• Miller and Charles (1991) – subset of RG-65, 30 English noun pairs (MC - 30)

• Finkelstein et al. (2002) – 353 word pairs (Fin1-153 and Fin2-200)

• Yang and Powers (2006) – 130 verb pairs (YP-130)

Problems with current datasets

• Part of speech limitation• Focus on semantic similarity instead of

relatedness• Size of dataset usually very small. Constructed

manually. Labor intensive.• Only general terms are included. Lack of

domain specific terms• Provides no insight into the type of SR

Survey Framework• Was created using 30 word pairs from Miller

and Charles (1991) dataset• Participants were asked to rate the

relatedness on a scale of 0 – 4, 0 being not related at all and 4 being highly related

• They were also asked to specify the kind of relationship

• They were made aware of the fact that 2 words may be related in a variety of ways – Synonymy, Antonymy, Frequent association, is a, part of, domain related etc.

Survey Framework

• Was conducted among students of IIT Bombay (particularly with a computer science & linguistics background)

• 55 students participated in the survey• Was created using Java Servlet and Tomcat

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ResultsSerial No. Word pair MC Original (38) MC New (55)

1 Car - Automobile 3.92 3.65

2 Gem - Jewel 3.84 3.22

3 Journey - Voyage 3.84 3.25

4 Boy - Lad 3.76 3.27

5 Coast - Shore 3.7 3.27

6 Asylum - Madhouse 3.61 2.14

7 Magician - Wizard 3.5 2.85

8 Midday - Noon 3.42 3.25

9 Furnace - Stove 3.11 2.34

10 Food - Fruit 3.08 2.78

Results

Serial No. Word Pair MC Original (38) MC New (55)

11 Bird - Cock 3.05 2.74

12 Bird - Crane 2.97 2.47

13 Tool - Implement 2.95 1.93

14 Brother - Monk 2.82 1.02

15 Lad - Brother 1.66 0.82

16 Crane - Implement 1.68 1.05

17 Journey - Car 1.16 2.18

18 Monk - Oracle 1.1 1.22

19Cemetery - Woodland 0.95 0.8

20 Food - Rooster 0.89 1.31

Results

Serial No. Word Pair MC Original (38) MC New (55)

21 Coast - Hill 0.87 1.2

22 Forest - Graveyard 0.84 0.74

23 Shore - Woodland 0.63 0.74

24 Monk - Slave 0.55 0.67

25 Coast - Forest 0.42 0.85

26 Lad - Wizard 0.42 0.49

27 Chord - Smile 0.13 0.58

28 Glass - Magician 0.11 0.82

29 Rooster - Voyage 0.08 0.24

30 Noon - String 0.08 0.31

Graph

Correlation Coefficient

Correlation between MC new and original = 0.91 – quite strong

Correlation(X,Y)(x x)(y y)(x x)2 (y y)2

Graph

Graph

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