1. Improved TF-IDF Ranker
Presentation By,
Muralidhar Chouhan

2. Contents Introduction Outline of our approach Background
Tf-Idf ranker
Semantic similarity between sentences
Details of our approach
Results
Conclusion
References

3. Introduction
Traditional information retrieval systems are particularly susceptible to all the problems posed by the richness of natural language.
In particular multitude of ways in which the same concepts can be described.
Overall context of the user input and the document is ignored.
Traditional TF IDF Ranker ignores the relatedness of concepts. Searches for the exact word match.
Introduction of semantic analyzer will improve the performance.

4. Introduction (cont..)
Aim of the project is to use traditional TF IDF ranker along with semantic analyzer to retrieve the documents. And to compare the performance of the new system with the traditional tf idf ranker.

5. Introduction (cont..) This project uses,
Text Retrieval Conference (TREC) data set named Confusion track for validation [6].
Wordnet lexical database
.NET framework (wordnet .net)

6. Outline of our approach
Input Query
Documents
Pre-processor
Primary filter
Document s
TF IDF Ranker
Doc ID, Weight pairs
Final Docs
Traditional TF IDF Ranker

7. Outline of our approach (cont..)
Input Query
Document s
Pre-processor
Primary filter
Document s
Semantic similarity
TF IDF Ranker
Doc ID, Weight pairs
Final Docs
TF IDF Ranker with introduction of Semantic knowledge

8. Outline of our approach (cont..)
Docs got from traditional tf idf approach
Input Query
Documents
Pre-processor
Corpus
Word,DF pairs
TF-IDF Ranker II
DocID, Keywords
Find the Keywords from each doc
Use Tf and Df (use Corpus)
Wordnet semantic Analyzer
Doc ID, Semantic score
Final Docs

9. Outline of our approach (cont..)
Pre-processor
Tokenize
Remove stop words

10. Background Tf-Idf ranker:
Tf-idf ranker is used as a weighting factor in information retrieval and text mining.
Terms that appear often in a document should get high weights.
The more often a document contains a term, the more likely that the document is about the term. It is captures using Term frequency (TF).
Terms that appear in many documents should get a low weight, which is captured using Inverse Document Frequency (IDF).
The weight of a term in a document is calculated using below formula [5],
Wi,j=TFi,j * log (N/DFi)

11. Semantic similarity between sentences:
Semantic similarity between sentences is calculated using semantic information and the word order information.
This project has used an implementation which calculates the semantic relatedness between two set of strings.
The implementation uses Wordnet lexical database, to calculate the semantic relatedness.
The score lies between 0 and 1. 0 representing least similarity score. 1being highest.

12. Wordnet:
Wordnet is the product of a research project at Princeton University [4].
Information in Wordnet is organized around logical groupings called synsets.
Each synset consists of a list of synonymous word forms and semantic pointers that describe relationships between the current synset and other synsets.
In Wordnet, each part of speech words (nouns/verbs...) are organized into taxonomies where each node is a set of synonyms (synset) represented in one sense.

13. Wordnet (cont..)
If a word has more than one sense, it will appear in multiple synsets at various locations in the taxonomy.
Wordnet defines relations between synsets and relations between word senses. A relation between synsets is a semantic relation, and a relation between word senses is a lexical relation.

14. Wordnet (cont..) For example,
The shortest path between male and female in Fig. 1 is male-person-female, the minimum path length is 2.
The minimum path length between female and teacher is 5.

15. Details of our approach
Traditional TF-IDF Ranker
Step1:Preprocess input query
Tokenization
Remove stop words
Step2: Apply Tf-Idf ranker
TF-Idf ranker would identify number of times each word appears in each of the documents as shown below.
Where TF­ij is the term frequency of word wi in document Dj.
DFi indicates document frequency of word Wi in document collection D1 D2 D3
, , DN DF W1
TF11
TF12
TF1N
DF1 W2
TF21
TF22
TF2N
DF2 W3
TF31
TF32
TF3N
DF3 : Wn
TFn1
TFn2
TFnN
DFn

16. Details of our approach(cont..)
Calculating the weight:
The weight of each word is calculated using below formula.
Wi,j=TFi,j * log (N/DFi) D1 D2 D3
, , DN DF W1
W11
W12
W1N
DF1 W2
W21
W22
W2N
DF2 W3
W31
W32
W3N
DF3 : Wn
Wn1
Wn2
WnN
DFn
Weight sum S1 S2 SN

17. Details of our approach(cont)
Step3 : Retrieve the documents Sort all the documents according to the weights. Pick top Q documents for further processing. Q is chosen such as the weight of each document crosses a particular threshold d1. Improved TF-IDF Ranker Step1: We choose top S from the step3 of previous method. Here we use another threshold d2(d2<d1) to get the set of docs for further processing. Step2: Extract the keywords (Words which have high TF and low DF) from each document.
Doc DF
Weight W1
TF1
DF1
We1 W2
TF2
DF2
We2 W3
TF3
DF3
We3 : Wn
TFn
DFn
Wen

18. Details of our approach(cont)
Corpus containing IDF (logN/DF) of each word from docs

19. Details of our approach(cont..)
Step 3: For each document, calculate the semantic similarity score between its keyword set and the input query. Step 4: Sort the docs w.r.t to score. Eliminate the docs with score less than a specified threshold (b=0.5). Step 5: Display the docs.

20. Confusion Track result set

21. Results: Old system vs New system
Results(cont..)
Results: Old system vs New system

22. Calculating precision & recall for 10 queries
Results(cont..)
Calculating precision & recall for 10 queries

23. Precision& Recall bar chat: Old system vs New system
Results(cont..)
Precision& Recall bar chat: Old system vs New system

24. Screenshots
Traditional IF IDF Ranker

25. Screenshots(cont..)
Improved IF IDF Ranker(with semantic knowledge)

26. Conclusion
This project has improvised traditional TF-IDF ranker by introducing Semantic analyzer.
Successfully showed that, using semantic analyzer has good precision and recall values.
Next, it used a dataset from Text Retrieval Conference Data (TREC) to validate the project.
One limitation of Tf-Idf Ranker is, terms that occur in query input text but that cannot be found in documents gets zero scores.

27. References
[1] R. Rada, H. Mili, E. Bichnell, and M. Blettner, “Development and Application of a Metric on Semantic Nets,” IEEE Trans. System, Man, and Cybernetics, vol. 9, no. 1, pp , 1989.
[2] Li, Yuhua,et.al, “Sentence Similarity Based on Semantic Nets and Corpus Statistics,” IEEE Trans on knowledge and data engineering, vol 18, no.8,2006.
[3] Dao, Thanh, Troy Simpson, “Measuring similarity between the sentences” .Web.
[4] R. Richardson, A. F. Smeaton and J. Murphy, “Using WordNet as a Knowledge Base for Measuring Semantic Similarity between Words,” School of Computer Applications, Dublin City University.Web.
[5] TfIdf Ranker, ‘ .web.
[6] Confusion track, TREC dataset ‘ .Web.

28. Thank you 