Question-answering systems are referred to as intelligent systems that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base. So the accuracy of a question-answering system to provide a correct response depends on the rules or facts stored in the knowledge base.

One of the many issues involved in a question-answering system is how the responses and questions would be represented in the system. Responses may be retrieved and then represented using text summarization or parsing. Another issue involved in the question-answering system is how the questions and the corresponding answers are represented in a knowledge base.

To build a question-answering system, various approaches, such as the named entity recognition, information retrieval, information extraction, and so on, can be applied.

A question-answering system involves three phases:

Extraction of facts is performed in order to understand domain-specific data and generate a response for a given query.

Extraction of facts can be performed in two ways using: extraction of entity and extraction of relation. The process of extraction of entity or extraction of proper nouns is referred to as NER. The process of extraction of relation is based on the extraction of semantic information from the text.

Understanding of questions involves the generation of a parse tree from a given text.

The generation of answers involves obtaining the most likely response for a given query that can be understood by the user.

Let's see the following code in NLTK that can be used to accept a query from a user user. This query can be processed by removing stop words from it so that information retrieval can be performed post processing:

import nltk
from nltk import *
import string
print "Enter your question"
ques=raw input()
ques=ques.lower()
stopwords=nltk.corpus.stopwords.words('english')
cont=nltk.word_tokenize(question)
analysis_keywords=list( set(cont) -set(stopwords) )