Kritische Diskussion weiter ausformuliert.

prosem/prosempaper.tex

@@ -279,17 +279,23 @@
 \label{sec:critDiscussion}
 
 	Now that both methods have been presented with one selected approach each, it is time to discuss them critically. The CYK algorithm solves many problems like ambiguity; at least to a certain degree. But it also is problematic, because of the restriction to CNF. While in theory every context-free grammar can be converted to CNF, in practice it poses ``some non-trivial problems''\footnote{\cite{Jurafsky2009b}, page 475}. One of this problems can be explored in conjunction with the second presented method (semantic analysis). ``[T]he conversion to CNF will complicate any syntax-driven approach to semantic analysis''\footnote{\cite{Jurafsky2009b}, page 475}. A solution to this problem is some kind of post-processing in which the trees are converted back to the original grammar.\cite{Jurafsky2009b} Another option is to use a more complex dynamic programming algorithm that accepts any kind of context-free grammar. Such an algorithm is the ``Earley Algorithm''\footnote{\cite{Jurafsky2009b}, page 477}.
-
-	\begin{itemize}
-		\item	compares syntactic parsing and semantic analysis based on these criteria: meaning, complexity, usability for actual communication
-	\end{itemize}
-
+	
+	The syntax-driven semantic analysis, as it has been presented, is a powerful method that is easy to understand. But it has one essential problem. It relies upon an existing set of grammar rules with semantic attachments to them. In a real world example such a table would contain thousands of grammar rules. While it is relatively easy to compute the final meaning representation with such a given table, it is very hard to create the table in the first place. The difficulty to create this table is split into two main issues. The first one being that you must find a grammar specification that fits all your use cases. This problem applies for the syntactic parsing as well. The second issue is that one has to find out the semantic attachments to the grammar rules.
+	
+	This initial workload to create a state, in which the semantic analysis works, is a unique effort. In a restricted environment with a limited set of words and topics, this workload is of low importance. Even if it takes one month to create such a table by hand or by computing it, the subsequent analysis of input based on this table is rather quick and the initial workload is therefore acceptable. But this is only true for restricted environments. If someone tried to use syntax-driven semantic analysis for the complete language of modern English, the creation of such a table would outweigh any possible usage.
+	
+	Comparing the complexity of the two methods it shows a mirror-like image. For the parsing the creation of the grammar is comparatively easy. The presented CYK algorithm works with context-free grammars which are a very restricted set compared to natural languages. But even within these context-free grammars there are ambiguities inside the texts themselves. The creation of the parse trees is therefore more of a problem.
+	
+	Syntax-driven semantic analysis on the other hand requires a decent amount of work to add semantic attachments to grammar rules. But once this has been done, it works very fast.
+	
+	Both methods require a unique work for one specific usage. This unique workload is the grammar creation for the parsing and the extension of the grammar with semantic attachments for the semantic analysis. The less restricted the usage environment, the more complex the initial workload becomes. The same is true for the recurring workload inside one specific usage.
+	
+	Judging by the state-of-the-art of computer technology, parsing does still pose a significant challenge once the restricted field of programming languages is left. The semantic analysis as the second method in the chain has therefore even more problems to date. As the presented syntax-driven approach does only work with parse trees, a semantic analysis can only be undertaken once the syntactic parsing succeeds. The dream of a sentient AI capable of communicating with humans in natural language is therefore far from being reached, judging by the previously described problems alone (leaving out all other problems related with this dream).
+	
 \section{Conclusion}
 \label{sec:concl}
 
-	\begin{itemize}
-	 	\item	
-	 \end{itemize}
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