The document outlines a doctoral dissertation defense presentation on using semantic relatedness to evaluate course equivalencies. The presentation includes an introduction, outlines knowledge sources and related work, describes two approaches to measuring semantic relatedness between courses, and discusses experimental results comparing the approaches.

1. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Semantic Relatedness for Evaluation of Course
Equivalencies
Doctoral Dissertation Defense
Beibei Yang
Department of Computer Science
University of Massachusetts Lowell
July 23, 2012

2. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Outline
1 Introduction
2 Knowledge Sources
3 Related Work
4 First Approach
5 Second Approach
6 Summary

3. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
NLP and Education
Many NLP techniques have been adapted to the education field for:
automated scoring and evaluation
intelligent tutoring
learner cognition
However, few techniques address the identification of transfer
course equivalencies.

4. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Why is it important to suggest transfer course
equivalencies?
National Association for College Admission Counseling, 2010
“. . . less attention is focused on the transfer admission process,
which affects approximately one-third of students beginning at
either a four- or two-year institution during the course of their
postsecondary careers.”
National Center for Education Statistics, 2005
“For students who attained their bachelor’s degrees in 1999–2000,
59.7 percent attended more than one institution during their
undergraduate careers and 32.1 percent transferred at least once.”

5. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
UML’s course transfer dictionary

6. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Course descriptions
C1 : Analysis of Algorithms
Discusses basic methods for designing and analyzing efficient algorithms emphasizing
methods used in practice. Topics include sorting, searching, dynamic programming,
greedy algorithms, advanced data structures, graph algorithms (shortest path,
spanning trees, tree traversals), matrix operations, string matching, NP completeness.
C2 : Computing III
Object-oriented programming. Classes, methods, polymorphism, inheritance.
Object-oriented design. C++. UNIX. Ethical and social issues.
f : (C1 , C2 ) → n, n ∈ [0, 1] (1)
C1 is a course from an external institution.
C2 is a course offered at UML.
Slide 34

7. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Knowledge Acquisition Bottleneck
Semantic relatedness measures that rely on a traditional knowledge
base usually suffer the knowledge acquisition bottleneck.
Knowledge acquisition is difficult for an expert
system [HRWL83]:
Representation mismatch: the difference between the way a human
expert states knowledge and the way it is represented in the system.
Knowledge inaccuracy: the difficulty for human experts to describe
knowledge in terms that are precise, complete, and consistent
enough for use in a computer program.
Coverage problem: the difficulty of characterizing all of the relevant
domain knowledge in a given representation system, even when the
expert is able to correctly verbalize the knowledge.
Maintenance trap: the time required to maintain a knowledge base.

8. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Semantic Relatedness
Three terms have been used interchangeably in related literature:
semantic relatedness, semantic similarity, and semantic distance.
Semantic Distance
Semantic Relatedness
Semantic Similarity
Figure : The relations of semantic distance, semantic relatedness, and
semantic similarity [BH06].

9. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Semantic Similarity versus Semantic Relatedness
Semantic Similarity
animal cat close
human cat distant
Semantic Relatedness
cat paw close
cat hand distant

10. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Popular Knowledge Sources
1 Lexicon-based Resources
Dictionaries
Thesauri
WordNet
Cyc
2 Corpus-based Resources
Project Gutenberg
British National Corpus
Penn Treebank
3 Hybrid Resources
Wikipedia
Wikitionary

11. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Related Work on Semantic Relatedness
1 Lexicon-based
Dictionary [KF93]
Thesaurus [MH91]
WordNet [WP94, LC98, HSO98, YP05]
2 Corpus-based
Query Expansion [SH06, BMI07, CV07]
LSA [LFL98]
HAL [BLL98]
PMI-IR [Tur01]
ESA (Wikipedia) [GM07, GM09]
3 Hybrid
Information Content [Res95]
Distributional profiling [Moh06, Moh08]
Li et al. [LBM03, LMB+ 06]
Ponzetto and Strube (Wikipedia) [PS07]

12. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
A Fragment of the WordNet Taxonomy
entity.n.01
physical entity.n.01
❳
❢❢❢❢❢ ❳❳❳❳❳❳❳❳❳
❢❢❢❢❢ ❳❳❳❳❳
❢❢❢❢❢
object.n.01
❳ matter.n.03
❳❳
❢❢❢❢❢ ❳❳❳❳❳❳❳❳❳ ❳❳❳❳❳
❢❢❢❢❢ ❳❳❳❳❳ ❳❳❳❳❳
❢❢❢❢❢ ❳❳❳
part.n.02 whole.n.02 solid.n.01
component.n.03 artifact.n.01 crystal.n.01
crystal.n.02 decoration.n.01 gem.n.02
piezoelectric crystal.n.01 adornment.n.01 transparent gem.n.01
jewelry.n.01
❳ diamond.n.02
❢❢❢❢❢ ❳❳❳❳❳❳❳❳❳
❢❢❢❢❢ ❳❳❳❳❳
❢❢❢❢❢
bracelet.n.02 necklace.n.01

13. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
The First Approach
1 Semantic relatedness between two concepts: based on
their path length and the depth of their common ancestor in
the WordNet taxonomy.
2 Semantic relatedness between two words: based on the
previous step, and includes POS and WSD.
3 Semantic relatedness between two sentences: constructs
two semantic vectors, and takes into account the information
content.
4 Word order similarity (optional): “a dog bites a man” & “a
man bites a dog”
5 Semantic relatedness between paragraphs
6 Semantic relatedness between courses

14. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Concept Relatedness
Path function:
f1 (p) = e−αp (α ∈ [0, 1]) (2)
Depth function:
eβh − e−βh
f2 (h) = (β ∈ [0, 1]) (3)
eβh + e−βh
Semantic relatedness between concepts c1 and c2 :
fword (c1 , c2 ) = f1 (p) · f2 (h) (4)

15. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Semantic Relatedness Between Words
Algorithm 1 Semantic Relatedness Between Words
1: If two words w1 and w2 have different POS, consider them se-
mantically distant. Return 0.
2: If w1 and w2 have the same POS and look the same but do not
exist in WordNet, consider them semantically close. Return 1.
3: Using either maximum scores or the first sense heuristic to per-
form WSD, measure the semantic relatedness between w1 and
w2 using Equation 4 .
4: Using the same WSD strategy as the previous step, measure the
semantic relatedness between the stemmed w1 and the stemmed
w2 using Equation 4 .
5: Return the larger of the two results in steps (3) and (4), i.e.,
the score of the pair that is semantically closer.

16. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Construct a List of Joint Words
To measure the semantic relatedness between sentences S1 and
S2 , first join them into a unique word set S, with a length of n:
S = S1 ∪ S2 = {w1 , w2 , . . . wn }. (5)
S1 : introduction to computer programming
S2 : introduction to computing environments
S: introduction to computer programming computing environments

17. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Construct a Lexical Semantic Vector
Algorithm 2 Lexical Semantic Vector s1 for S1
ˆ
1: for all words wi ∈ S do
2: if wi ∈ S1 , set sˆ = 1 where sˆ ∈ s1 .
1i 1i ˆ
3: if wi ∈ S1 , the semantic relatedness between wi and each
/
word w1j ∈ S1 is calculated using algorithm 1 . Set sˆ to the
1i
highest score if the score exceeds a preset threshold δ (δ ∈
[0, 1]), otherwise sˆ = 0.
1i
4: Let γ ∈ [1, n] be the maximum number of times a word w1j ∈
S1 is chosen as semantically the closest word of wi . Let
the semantic relatedness of wi and w1j be d, and f1j be
the number of times that w1j is chosen. If f1j > γ, set
sˆ = d/f1j to give a penalty to w1j . This step is called
1i
ticketing.
5: end for

18. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
First-level Sentence Relatedness
TF-IDF:
N
T F IDF (wi ) = tfi · idfi = tfi · log (6)
dfi
Semantic vector SV1 for sentence S1 :
SV1i = sˆ ·(T F IDF (wi )+ )·(T F IDF (w1j )+ ),
1i (i ∈ [1, n], j ∈ [1, t])
(7)

19. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
First-level Sentence Relatedness
(1) SV1 · SV2
fsent (S1 , S2 ) = (8)
||SV1 || · ||SV2 ||

20. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Second-level Sentence Relatedness
Word order similarity:
||Q1 − Q2 ||
forder (S1 , S2 ) = 1 − (9)
||Q1 + Q2 ||
Q1 , Q2 : word order vectors of S1 and S2 .
Second-level Sentence Relatedness:
(2) (1)
fsent (S1 , S2 ) = τ ·fsent (S1 , S2 )+(1−τ )·forder (S1 , S2 ), τ ∈ [0, 1]
(10)

21. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Semantic Relatedness Between Paragraphs
n m
i=1 (maxj=1 fsent (s1i , s2j )) · Ni
fpara (P1 , P2 ) = n (11)
i=1 Ni
Algorithm 3 Semantic Relatedness for Paragraphs
1: If deletion is enabled, given two course descriptions, select the one with
fewer sentences as P1 , and the other as P2 . If deletion is disabled,
select the first course description as P1 , and the other as P2 .
2: for each sentence s1i ∈ P1 do
3: Calculate the semantic relatedness between sentences using
equation 10 for s
1i and each of the sentences in P2 .
4: Find the sentence pair s1i , s2j (s2j ∈ P2 ) that scores the highest.
Save the highest score and the total number of words of s1i and
s2j . If deletion is enabled, remove sentence s2j from P2 .
5: end for
6: Collect the highest score and the number of words from each run.
Use their weighted mean from equation 11 as the semantic relatedness
between P1 and P2 .

22. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Semantic Relatedness Between Courses
fcourse (C1 , C2 ) = θ·fsent (T1 , T2 )+(1−θ)·fpara (P1 , P2 ), θ ∈ [0, 1]
(12)

23. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Data sets
Data Sets MCC Courses UML Courses Total
Small 25 24 49
Medium 55 50 105
Large 108 89 197
Table : Number of courses in the data sets

24. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Experimental Results
Compared against the method by Li et al. [LMB+ 06] and
TF-IDF [SB88]:
Accuracy Comparison Average ranks of the real equivalent courses
100 Enable word order Enable word order
Disable word order 20 Disable word order
90 Best case TFIDF TFIDF
Li Li
80
15
70
Average rank
Accuracy
60 10
50
40 5
30 Best case
20 0
49 105 197 49 105 197
Number of documents Number of documents

25. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Experimental Results
Performance of two word sense disambiguation algorithms:
Accuracy Comparison of WSD
100
90 Best case
80
70
Accuracy
60
50
40
30 FIRST SENSE
MAX
20
49 105 197
Number of documents

26. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
What’s Wrong with WordNet?
91.304 Foundations of Computer Science
A survey of the mathematical foundations of Computer Science. Finite
automata and regular languages. Stack Acceptors and Context-Free
Languages. Turing Machines, recursive and recursively enumerable sets.
Decidability. Complexity. This course involves no computer programming.
64 unfiltered words fetched from WordNet
acceptor, adjust, arrange, automaton, basis, batch, bent, calculator, car,
class, complexity, computer, countable, course, determine, dress, even,
finite, fix, foundation, foundation garment, fructify, hardening, imply,
initiation, involve, jell, language, linguistic process, lyric, machine,
mathematical, naturally, necessitate, numerical, path, place, plant,
push-down list, push-down storage, put, recursive, regular, review, rig,
run, science, set, set up, sic, sketch, skill, smokestack, specify, speech,
stack, stage set, surveil, survey, terminology, turing, typeset,
unconstipated, view.

27. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
What’s Wrong with WordNet?
91.304 Foundations of Computer Science
A survey of the mathematical foundations of Computer Science. Finite
automata and regular languages. Stack Acceptors and Context-Free
Languages. Turing Machines, recursive and recursively enumerable sets.
Decidability. Complexity. This course involves no computer programming.
18 articles fetched from Wikipedia using the second approach
Alan Turing, Algorithm, Automata theory, Complexity, Computer,
Computer science, Context-free language, Enumeration, Finite set,
Finite-state machine, Kolmogorov complexity, Language, Machine,
Mathematics, Recursive, Recursive language, Recursively enumerable set,
Set theory.
Slide 33

28. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Growth of Wikipedia and WordNet over the years
Growth of English Wikipedia and WordNet
4000000
Articles in Wikipedia
3500000 Synsets in WordNet
3000000
Article/Synset count
2500000
2000000
1500000
1000000
500000
1992 1996 2000 2004 2008 2012
Year

29. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
WordNet versus Wikipedia
Fragments of WordNet and Wikipedia Taxonomies
WordNet [Root: synset(‘‘technology’’), #depth: 2]
# nodes: 25
Wikipedia [Centroid: ‘‘Category:Technology’’, #steps: 2]
# nodes: 3583

30. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Extract a Lexicographical Hierarchy from Wikipedia
1 Let’s assume the knowledge domain is specified, e.g.,
“Category:Computer science.”
2 Choose its parent as the root, i.e., “Category:Applied
sciences.”
3 Use a depth-limited search to recursively traverse each
subcategory (including subpages) to build a lexicographical
hierarchy with depth D.

31. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Growth of the Hierarchy from Wikipedia
Depth: 3
Depth: 1 Depth: 2 Total Nodes: 64,407
Total Nodes: 72 Total Nodes: 4,249
Growth of the lexicographical hierarchy constructed from Wikipedia, illustrated in
circular trees. A lighter color of the nodes and edges indicates that they are at a
deeper depth in the hierarchy.

32. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Lexicographical Hierarchy constructed from Wikipedia
Depth (D) Number of concepts at this level
1 71
2 4,177
3 60,158
4 177,955
5 494,039
6 1,848,052
Table : Number of concepts for each depth in the “Category:Applied
sciences” hierarchy.
The hierarchy only include 1,534,267 distinct articles, out of
5,329,186 articles in Wikipedia. ⇒ Over 71% Wikipedia
articles are eliminated.

33. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Generate Course Description Features
Algorithm 4 Feature Generation (F ) for Course C
1: Tc ← ∅ (clear terms), Ta ← ∅ (ambiguous terms).
2: Generate all possible n-grams (n ∈ [1, 3]) G from C.
3: Fetch the pages whose titles match any of g ∈ G from Wikipedia redirection
data. For each page pid of term t, Tc ← Tc ∪ {t : pid}.
4: Fetch the pages whose titles match any of g ∈ G from Wikipedia page title
data. If a disambiguation page, include all the terms this page refers to. If a
page pid corresponds to a term t that is not ambiguous, Tc ← Tc ∪{t : pid},
else Ta ← Ta ∪ {t : pid}.
5: For each term ta ∈ Ta , find the disambiguation that is on average most
related using Equation 4 to the set of clear terms. If a page pid of ta is
on average the most related to the terms in Tc , and the relatedness score is
above a threshold δ (δ ∈ [0, 1]), set Tc ← Tc ∪ {ta : pid}. If ta and a clear
term are different senses of the same term, keep the one that is more related
to all the other clear terms.
6: Return clear terms as features.
Slide 27

34. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Example of Course Features
C1 : {1134:“Analysis”, 775:“Algorithm”}
{41985:“Shortest path problem”, 597584:“Tree traversal”, 455770:“Spanning tree”,
18955875:“Tree”, 1134:“Analysis”, 18568:“List of algorithms”,
56054:“Completeness”, 775:“Algorithm”, 144656:“Sorting”, 8519:“Data structure”,
93545:“Structure”, 8560:“Design”, 18985040:“Data”}
C2 : {5213:“Computing”}
{21347364:“Unix”, 289862:“Social”, 9258:“Ethics”, 6111038:“Object-oriented
design”, 5311:“Computer programming”, 72038:“C++”, 27471338:“Object-oriented
programming”, 8560:“Design”}
Slide 6

35. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Lexical Semantic Vector
An algorithm similar to Algorithm 2 is used to determine each
value of an entry of the lexical semantic vector sˆ for features
1i
F1 .
A semantic vector is defined as:
SV1i = sˆ · I(ti ) · I(tj )
1i (13)

36. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Information Content
Information content I(t) of a term t:
I(t) = γ · Ic (t) + (1 − γ) · Il (t). (14)
Category information content Ic (t):
log(siblings(t) + 1)
Ic (t) = 1 − , (15)
log(N )
Linkage information content Il (t):
inlinks(pid) outlinks(pid)
Il (t) = 1 − · , (16)
M AXIN M AXOU T

37. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Determine Course Relatedness
SV1 · SV2
f (C1 , C2 ) = . (17)
||SV1 || · ||SV2 ||
f (T1 , T2 ) · (||FT 1 || + ||FT 2 ||) + f (C1 , C2 ) · (||FC1 || + ||FC2 ||)
f (course1 , course2 ) = +Ω,
||FT 1 || + ||FT 2 || + ||FC1 || + ||FC2 ||
(18)

38. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Experimental Results
Randomly select 25 CS courses from 19 universities that can
be transferred to UML according to the transfer dictionary.
Each transfer course is compared to all 44 CS courses offered
at UML.
The result is considered correct if the real equivalent course at
UML is among the top 3 in the list of highest scores.
Algorithm Accuracy
Proposed approach 72%
Li et al. [LMB+ 06] 52%
TF-IDF 32%
Table : Accuracy of the second approach against those of Li et al., and
TFIDF

39. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Experimental Results
Algorithm Pearson’s correlation p-value
TF-IDF 0.730 2 · 10−6
Li et al. [LMB+ 06] 0.570 0.0006
Proposed approach (Features) 0.845 1.13 · 10−9
Proposed approach (Features + IC) 0.851 6.65 · 10−10
Table : Pearson’s correlation of course relatedness scores with human
judgments.

40. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Sensitivity Test
Testing the Sensitivity of Parameters α, β, and δ
1.0
Pearson Correlation When α Changes (β =0.5, δ =0.2)
0.8
Pearson correlation
0.6
0.4
0.2
0.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
α
1.0
Pearson Correlation When β Changes (α =0.2, δ =0.2)
0.8
Pearson correlation
0.6
0.4
0.2
0.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
β
1.0
Pearson Correlation When δ Changes (α =0.2, β =0.5)
0.8
Pearson correlation
0.6
0.4
0.2
0.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
δ

41. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Summary
Highlight the problem of suggesting transfer course
equivalencies.
Proposes two semantic relatedness measures to tackle the
problem.
A semantic relatedness measure based on traditional
knowledge sources can be adapted.
Wikipedia is a better knowledge source compared to
traditional knowledge sources.
A domain-specific semantic relatedness measure built on top
of Wikipedia suits well for suggesting transfer course
equivalencies.
Provides a human judgment data set over 32 pairs of courses:
http://bit.ly/semcourse.

42. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
Published Literature
Using Semantic Distance to Automatically Suggest Transfer Course
Equivalencies
Beibei Yang and Jesse M. Heines
ACL-HLT 2011: Proceedings of the Sixth Workshop on Innovative
Use of NLP for Building Educational Applications (BEA-6)
Association for Computational Linguistics
Domain-Specific Semantic Relatedness from Wikipedia: Can a
Course be Transferred?
Beibei Yang and Jesse M. Heines
NAACL-HLT 2012 Student Research Workshop

43. Introduction Knowledge Sources Related Work First Approach Second Approach Summary References
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