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Open Domain Question Answering System using Cognitive Computing
Contents1. Introduction2. Methodologies3. Proposed Architecture4. Implementation5. Advantages6. Disadvantages7. Conclusion
1. IntroductionOpen Domain Question Answering System is the new way of searching for the information present on the internet .
Main objective: To retrieve correct answers to the queries posed by the users in search engines.
Data source:world wide web or local database collection.
Improvised in an efficient way by using:➔ Cognitive Computing
What is Cognitive Computing and how is it used in this system to make it more efficient?
2.Methodologies1. Personalized e-learning recommender.
2. Answering English questions by computer.
3. Template based question answering system.
4. DBpedia and Freebase.
5. The Proposed System
3. Proposed ArchitectureTraditional question answering questions are of two types:
➔ Open Domain System*anything and everything can be answered.
➔ Closed domain system*data is restricted to particular domain.
Modelling of question answering system at psychological level
Question Interpretation
Question category Identification
Apply question answering procedure to relevant knowledge data structures
Articulate answers
4. Implementation1. Indexing
2. Question processing
3. Query generation
4. Candidate Answer Extraction
5. Answer Ranking and scoring
Indexing● collecting, parsing, and storing data
to facilitate fast and accurate information retrieval.
● Documents cleaned from SGML markup.
● Splitting document up into tokens.● Storing in inverted index.
Question processing
● Answer extraction in a collection of large number of texts and documents.
Two phases
1.parsing the query 2. Computing the answer type
Classify the question
Identify the answer
Light weight process
● To generate a query that will retrieve candidate
passage.
● Parts of speech tagging.
● Matching it with passage.
Query generation
Candidate Answer Extraction
Answer Extraction
Based on textual pattern
Similarity between query and candidate sentences
Feature based method
Answer ranking and scoring● Confidence score is generated on various parameters which is
used for ranking.
Parameters:
● Number of consecutive terms
which are present both in
passage and query.
● Synonyms of query.
ArchitectureUser query
Analyze natural language query
Tokenization and POS tagging
Extract keywords
Determine answer type
Execute Search
Answer Extraction
Answer ranking
qFinding conceptual terms
Passage retrieval
Query keywords
5.Advantages● User need not wade through huge
amount of data.
● It can interpret natural language.
● Time saving process
● Accurate answers
6.Disadvantages
● Database maintenance.
ConclusionMany users want to have answers of their queries posed in search bar as we humans do answer instead of browsing a collection of web documents returned as results.This is an efficient way of implementing it .In future work we can add speech recognition to identify the question from the end user.
Any Questions
?
THANK YOU!!