WEEK 6 READING NOTES

"This sort of thing is extremely hard. But I do not believe that we should therefore not attempt to do it or argue, in a supposedly more principled manner, that setups within which modern retrieval systems have to operate are so di use, or so variegated, that it is a fundamental mistake to address anything but the immediate D * Q * R environment from which solid, transportable, general-purpose retrieval system knowhow can be acquired. In fact, indeed, TREC's newer tracks subvert both of these arguments: even if the lawyers' interpretation of \relevant" as referencing might be inferable from assessment data samples, one feels rather less con dent about being able to infer, even with the best modern machine learning tools, that the name of the retrieval game is getting information that \appears reasonably calculated to lead to the discovery of admissible evidence"."

The development of TREC has faced several challenges by far. Some people may think that it is not necessary to spend too much time and money on the related research. However, it is unfair to think this way. What TREC could bring to us is far more than our imagination. TREC track has finished several significant tasks in information retrieval field. It helps developers deal with huge amount of data more efficiently and accurately. And the standard of TREC file makes random data more standardized and more recognizable. One of the biggest challenges of TREC is that it is hard to convert huge data into TREC. It might cost too much time and machines to reach the goal. Thus, the further development of TREC is absolutely needed. And the research is definitely valuable.