A Question Answering (QA) system is an Information Retrieval system which gives the answer to a question posed in natural language. For example, if you ask it Who wrote Hamlet?, it should answer Shakespeare. A few years ago (don’t ask me how many), search engines did not focus on language queries. Recently [sic], Google has started incorporating some NLP (Natural Language Processing) in their results. You can try it out by typing the same question in the search box yourself ( or clicking here ).
During my M.Phil. course, one of the tasks was to build a basic QA system and extend it however we liked. We used the TREC 8 dataset for evaluations. While building the system, I evaluated how current search engines (read Google) performed on this task. For this, I just queried the exact question and used the summaries of the top five results as answers. Evaluating at that time (2008), I got a Mean Reciprocal Rank (MRR) score of 0.212 over 198 questions. 156 questions had no answers found in top 5 responses.
This term, I am demonstrating for the same task. Demonstrators are usually PhD students who provide help and guidance to junior students. For pure geek fun and lack of better things to do while taking a break, I decided to quickly jolt down a JavaScript (read JQuery ) based QA system. This time, the resulting MRR score over 198 questions was 0.384 while only 79 questions had no answers found in top 5 responses.
The results show clearly that during the last two years, Google has significantly improved on answering NLP queries. In fact (IIRC), my baseline system back in 2008 (based on RMRS based matching of sentences from the top 100 documents returned by an IR system) could only achieve an MRR score of approximately 0.290, showing that the current results are much better than that baseline. I hope this decade sees some more developments/improvements in QA systems and I can ask a system What do you get if you multiply six by nine?
I’ve always said there was something fundamentally wrong with the universe. ~Arthur Dent