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Survey Analysis An attempt to develop an Intuition of Semantic Relatedness

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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? - PowerPoint PPT Presentation

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Page 1: Survey Analysis

Survey Analysis

An attempt to develop an Intuition of Semantic Relatedness

Page 2: Survey Analysis

Outline

• Motivation• Survey framework• Analysis

Page 3: Survey 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

Page 4: Survey Analysis

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)

Page 5: Survey Analysis

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

Page 6: Survey Analysis

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.

Page 7: Survey Analysis

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

container

Page 8: Survey Analysis

Screen Shot

Page 9: Survey Analysis

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

Page 10: Survey Analysis

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

Page 11: Survey Analysis

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

Page 12: Survey Analysis

Graph

Page 13: Survey Analysis

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

Page 14: Survey Analysis

Graph

Page 15: Survey Analysis

Graph

Page 16: Survey Analysis

Graph