The ever increasing need to provide a suitable derivative of a term governed by context where derivative can either stemmed word or hypernym of a word has spurred a lot of research activities in information retrieval communities. In this presentation, we are concerned with providing context centric derivatives of a term which can be useful in any search engine for obtaining better search results. Personalized Terms Derivative (PTD) provides derivative of terms based on the context surrounding the term. We also emphasize how the PTD (Personalized Terms Derivative) provides greater capabilities or enhance capabilities of an existing search engine like Solr and Elastic Search to perform boolean search effectively. This problem is a vital cog in the wheel of text analytics world. It can also be extended to improvise the result of keyword extraction, abstractive summarization, and POS parser tree.

1. Personalized Terms Derivative
Beyond Stemming
Root
Word 1
Word 2
Word 3
Root
Word 4
Word 5

2. What is PTD?
Semantic approach for Stemming
Capabilities to include Ontology and Semantic
dictionary like wordnet
Extends the meaning of stemming to hypernym and
user defined root word
Includes linguistic and morphological stemming
provided by Porter-Stemmer and K-Stem

3. Algorithm Steps
Preprocessing
Word Sense
Disambiguation
Sense Annotation Relationship K-Stem

4. Architecture Flow Diagram
Extract
Sentences
Lemmatized each word in
the Sentences
Named Entity Extraction ,
Tokenization
Annotation
For Each
Sentences
For Each Tokens
If
Term
= N-E
Word Sense Disambiguation
for each word in a sentence
Right sense of each word ,
POS Identification
Tagging
If
POS
= NN
For Each
Tokens
YES
YES
NO
Find Hypernym/Root word
NO (Till all terms
are processed
once)
Knowledge Data
Sources
Thesaurus
Dictionary,
Ontology
Create
Exclusion
List
K-Stemmer
If Root word
not found
Documents

5. Application Area
Search,
Index
Engine
(Solr, ES)
Personalized
Search
(Dictionary)
WEB
Search
(Google)
Targeted
Stemming
Text
Summarization
(Abstractive)

6. Comparison
Words Porter Stemmer PTD with Wordnet PTD with
Ontology
Source Link
oncology oncolog oncology medical
specialty
https://www.ncbi.nlm
.nih.gov/pmc/articles
/PMC3596793/
mutation mutation organism genetic variation https://www.ncbi.nlm
.nih.gov/pmc/articles
/PMC3596793/
apoptosis apoptosi cell death cell physiological
processes
https://www.ncbi.nlm
.nih.gov/pmc/articles
/PMC3596793/
biological biolog biological discipline https://www.ncbi.nlm
.nih.gov/pmc/articles
/PMC3596793/
immune immun immune pathogeneses https://www.ncbi.nlm
.nih.gov/pmc/articles
/PMC3596793/
cytarabine cytarabin cytarabine pyrimidine
nucleosides
https://www.ncbi.nlm
.nih.gov/pmc/articles
/PMC3596793/
metastasis metastasi pathological
process
cancer
progression
https://www.ncbi.nlm
.nih.gov/pmc/articles
/PMC2927383/

7. Demo
 Demo : http://apis.tekstosesense.com
 Source Code : https://github.com/TekstoSense/word-root-finder
 Home : http://www.tekstosense.com
 Source Code Home : https://github.com/TekstoSense/about-
tekstosense