polysemy tag detect in tag sets

Word Net and Wiki Based Approach for Finding Polysemy Tags
in a Tag Set
Authors: Saman Iftikhar, Fouzia Jabeen, Hira Nasir, Shahwana Fida
Presented By: Saman Iftikhar
Department of Computer Science,
Shaheed Benazir Bhutto Women University, Peshawar, Pakistan

Contents
 Introduction
 Problem Statement
 Significance
 Related Work
 Methodology
 Experimental Results
 Evaluation
 Summary
 Conclusion
 Future Directions
2ISCV The fourth International Conference on Intelligent Systems and Computer Vision 2020

Introduction
Tagging
Figure 1. Tagging[1]

Introduction…Cont
Collaborative Tagging
Figure 2. Collaborative Tagging[2]

Introduction…Cont
Figure 3. Apple fruit[3] Figure 4. Apple corporation[4]
 Polysemy

Introduction…Cont
 Impact of Polysemy Tags
 Decrease of accuracy
 Creates lot of confusion due to ambiguity and redundancy
 Complications in searching
 Browsing
 Gives wrong results
 Effects the performance of tag based recommendation system

Problem Statement
• To detect polysemy tag in a tag set
7
ISCV The fourth International Conference
on Intelligent Systems and Computer Vision
2020

Significance
 Brings precision in search results
 Just looking at the URL a user cannot understand the contents of a
resource
 The tags associated with it helps user to give initial understanding to know
what the resource is about
 However due to polysemy ambiguity is created which causes confusion in
knowing the resource
 By removing ambiguity user can have clear picture of contents of resource

Related Work
 We have studies the literature related to polysemy detection from
four angles:
 Modifying tagging architectures [5][6]
 Solving polysemy problem in WordNet [7][8]
 Polysemy reduction based approaches [9][10]
 Classification based approaches [11][12]

Related Work…Cont
 The literature review makes us to conclude
• Only WordNet is not enough to solve problem of polysemy.
• Therefore, in the proposed approach the Wikipedia is used with the combination of wordNet dictionary to
catch polysemy tags extensively.
– Wikipedia is used for two major reasons.
• First, WordNet itself has polysemy issues making it insufficient for polysemy detection.
• Secondly, folk tags are not necessarily pressed in Wordnet dictionary. However, wiki is updated
frequently so new vocabulary of user is available on wiki. For examples RSS (rich site summary)
tag is not presents in dictionary but have wiki page.
• In addition, visual tag dictionary is exploited to display images with tags for more clarity of meanings.
• These makes proposed approach novel against any other approaches done on same research topic.

Methodology
Figure 5. Methodology

Experimental Dataset
Figure 6: Retrieving Tags and URL's through RSS feed from Delicious[13]
 Tag sets along with URL’s

Results
Figure 7. Polysemy detect
through wordnet, and
wiki

Results…Cont
 Word Net alone is not able to dig out all polysemy
tags. In the experimental data three words that are
not detected by Word Net (e.g., rss, podcast and for
all) but have Wikipedia pages.
 So, the suggested approach improves accuracy in
polysemy detection which is not possible if we use
WordNet alone.
ISCV The fourth International Conference on Intelligent Systems and Computer Vision 2020 14

Evaluation
We have evaluated our results and tabulated to
show:
 Which type of polysemy tags are detected
 Which are not detected through our approach

Evaluation
Tag set URL’s Polysemy words Type of polysemy
podcast rss podcasting tips
tutorial knowledge
base
http://www.feedforall.com/rss
2html.php?XMLFILE=http://w
ww.feedforall.com/po...
1) podcast
2) rss
3) podcasting
4) base
1) Contrastive
2) Complementary
3) Contrastive
4) Complementary(Metaph
oric, Metonymy)
16
Table 1. Types of polysemy detected
ISCV The fourth International Conference on Intelligent Systems and Computer Vision 2020

Evaluation
Those types of polysemy which are not detected
 Autohyponymy
 Autoholonymy

Summary
 Polysemy in the tag set causes confusion and ambiguity.
 The approach we opted in this work makes use of WordNet
and Wikipedia.
 In addition, to improve the clarity visual tag dictionary is used
to associate images with each of the meaning.

Conclusion
 WordNet alone is not enough for providing us all the meanings of polysemy tags.
The recommended approach uses wiki in order to get complete extensive meaning
set of polysemy tags.
 After performing experiments, it is perceived that Visual tag dictionary (e.g.,
Merriam-Webster visual dictionary) is not extensive because it does not provide
images of all possible meanings of a tag.
 By using Word Net and Wiki the presented approach can detect some common
types of polysemy (e.g., contrastive polysemy and complementary polysemy).
These types are common in tag sets.
 The types of polysemy not detected by our approach are Autohyponymy,
Autoholonymy which we can say limitation of our work, but these types of
polysemy tags appear infrequently in tag sets. 19ISCV The fourth International Conference on Intelligent Systems and Computer Vision 2020

Future Directions
 The possible future work is to extend the work and
further enrich the presented approach to be able to
detect polysemy types that are uncommon in tag
sets of folksonomies.

References
1. https://www.google.com.pk/search?q=tagging&sxsrf=ACYBGNSpxp6gkpVw8rfMXgpU6L8PLdmurA:1581417063945&sour
ce=lnms&tbm=isch&sa=X&ved=2ahUKEwjo_tnSpcnnAhVRQhoKHbH3Au0Q_AUoAXoECBQQAw&biw=1366&bih=576[Acce
ssed : 2-june-2020]
2. https://www.google.com.pk/search?q=collaborative+tagging&tbm=isch&ved=2ahUKEwjHsJrUpcnnAhWLwYUKHXAOCGkQ
2-cCegQIABAA&oq=collaborative+tagging&gs_l=img.3..0i24.68745.76535..78161...1.0..0.353.3686.2-14j1......0....1..gws-
wiz-img.......35i39j0i67j0j0i7i30.kK_Km6DHPUk&ei=a4JCXofPBYuDlwTwnKDIBg&bih=576&biw=1366[Accessed : 2-june-
2020]
3. https://www.google.com.pk/search?q=polysemy+tag&tbm=isch&ved=2ahUKEwj1q8L6pcnnAhWF0oUKHb44C7oQ2-
cCegQIABAA&oq=polysemy+tag&gs_l=img.3..0.46753.53263..55415...1.0..4.335.4407.2-14j3......0....1..gws-wiz-
img.....10..35i39j35i362i39j0i67j0i24.9t2w-lqGeEE&ei=u4JCXrXiGoWllwS-8azQCw&bih=576&biw=1366[Accessed : 2-june-
2020]
4. https://www.google.com.pk/search?q=polysemy+tag&tbm=isch&ved=2ahUKEwj1q8L6pcnnAhWF0oUKHb44C7oQ2-
cCegQIABAA&oq=polysemy+tag&gs_l=img.3..0.46753.53263..55415...1.0..4.335.4407.2-14j3......0....1..gws-wiz-
img.....10..35i39j35i362i39j0i67j0i24.9t2w-lqGeEE&ei=u4JCXrXiGoWllwS-8azQCw&bih=576&biw=1366[Accessed : 2-june-
2020]
5. Freihat, A. A. An organizational approach to the polysemy problem in wordnet (Doctoral dissertation) (pp. 1-206).
University of Trento 2014.
6. A. Marchetti and M. Rosella, “SemKey : A Semantic Collaborative Tagging System ∗,” Buitelaar, Paul. “CORELEX An Ontol.
Syst. polysemous classes.” 221-235., vol. 7, pp. 8–12, 2007.
7. A. A. Freihat, “An Organizational Approach to the Polysemy Problem in WordNet,” 2014.

References
8. Freihat, A. A. An organizational approach to the polysemy problem in WordNet (Doctoral dissertation) (pp.
1-206). University of Trento 2014.
9. P. Buitelaar, “CoreLex : An Ontology of Systematic Polysemous Classes,” 1998, pp. 221–235.
10. A. A. Freihat, “Compound Noun Polysemy and Sense Enumeration in WordNet,” no. c, pp. 166–171, 2015.
11. K. Wan, A. Tan, and L. Chia, “A Latent Model for Visual Disambiguation of Keyword-based Image Search,”
vol. 2, no. June 2014, p. 9, 2009.
12. H. Chen, G. Bian, and W. Lin, “Resolving Translation Ambiguity and Target Polysemy in Cross-Language
Information Retrieval,” 1999, pp. 215–222.
13. Javier Parra-Arnau, “user select tags from resources,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 2014, pp.
180–193, 2014.

Thank you

polysemy tag detect in tag sets

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