disambiguation

Say I have an expression as follows (where ⨁ and ⨂ are binary operators which have the same precedence level but not the same associativity): x ⨁ y ⨂ z Would y belong to ⨁ or ⨂ , and based on what criteria? According to the Edsgar Dijkstra's Shunting-yard algorithm if neighboring two operators in an expressions have the same precedence level then the expression is disambiguated based on the associativity of the second operator. If the second operator is left associative then the operand belongs to the first operator. If the second operator is right associative then the operand belongs to the

I have a class which takes a size as a template parameter ( live demo ): template <std::size_t SIZE> class A { char b[SIZE]; } It have multiple constructors for different purposes: using const_buffer_t = const char (&)[SIZE]; using my_type = A<SIZE>; A() : b{} {} // (1) no params A(const_buffer_t) : b{} {} // (2) copy contents of given buffer A(const char * const) : b{} {} // (3) copy as many items as they fit into the size explicit A(const my_type &) : b{} {} // (4) copy constructor // (5) copy as many items as they fit into the size template <std::size_t OTHER_SIZE> A(const char (&)[OTHER

I'm documenting a Scala class with overloaded methods . How can I distinguish them when referring to them in scaladoc comments? For example, if I have /** * The most important method is [[Doc.foo]]. */ object Doc { def foo[A]: A = throw new UnsupportedOperationException; def foo[A,B >: A](x: A): B = x; } and run sbt doc I get Doc.scala:1: warning: The link target "Doc.foo" is ambiguous. Several (possibly overloaded) members fit the target: method foo[A,B>:A](x:A):B in object Doc [chosen] method foo[A]:Nothing in object Doc Using foo[A,B >: A] etc. to the link doesn't work. The following seems

What represents the state-of-the-art in Word Sense Disambiguation (WSD) software? What metrics determine the state-of-the-art, and what toolkits / open source packages are available? My list are not exhaustive but surely Googling for more will be better for your purposes. For softwares here's a short list, remember to CITE the relevant sources!!! GWSD: Unsupervised Graph-based Word Sense Disambiguation http://lit.csci.unt.edu/~rada/downloads/GWSD/GWSD.1.0.tar.gz SenseLearner: All-Words Word Sense Disambiguation Tool http://lit.csci.unt.edu/~rada/downloads/senselearner/SenseLearner2.0.tar.gz