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.

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