deletions | additions       diff --git a/The_standard_NLP_pro.tex b/The_standard_NLP_pro.tex  deleted file mode 100644  index bd6eb25..0000000  --- a/The_standard_NLP_pro.tex  +++ /dev/null 
...
The standard NLP problem which is closest to answering yes/no questions  is the so-called \textit{Recognizing Text Entailment} (RTE) task.  In the standard formulation,  we have a paragraph of context and a phrase that either is or isn't  \textit{entailed} , i.e.\ can be decided to be true just based on the context.  The most prominent effort in this area is probably the  \textsc{Excitement EOP} academic project,%  \footnote{\url{http://hltfbk.github.io/Excitement-Open-Platform/}}  which is a full-fledged  RTE pipeline in Java that implements several algorithms  in a common framework.  Typical state-of-art RTE algorithms work on the principle of parse tree  alignment --- grammar dependency tree of the hypothesis and each context  sentence is compared and we try to learn which changes in the tree might  keep entailment.  But as the RTE problems are quite hard and practical applications are limited,  this is not a very lively area of research per se.  diff --git a/layout.md b/layout.md  index f45779b..cf300fd 100644  --- a/layout.md  +++ b/layout.md 
...
When_considering_the.tex  subsectionVector.tex  subsectionNLP_for_Ye.tex  The_standard_NLP_pro.tex  With_this_in_mind.tex  beginitemize__item_t.tex  To_conclude_the_yesn.tex