Prosem: Einleitung und Fazit verbessert

* TODOs zur Einleitung hinzugefügt
* fehlendes Wort ergänzt in Einleitung
* allgemeine Verbesserung der Einleitung
* Verbesserung des zweitletzten Absatzes im Fazit

prosem/prosempaper.tex

@@ -196,6 +196,9 @@
 	
 	The input in this case is plain text, following the grammar of a natural language like English. Without loss of generality it is assumed that the input is syntactically correct and follows the grammar of the natural language. The computer therefore gets a certain amount of text that follows a specified grammar. The grammar of modern English is assumed for the scope of this paper. With this information available, the computer still knows nothing about the meaning of the text. You could ask for a hot chocolate or you could write nasty things, it won't make a difference at this point.
 	
+	%TODO explain *why* we use these methods and why they are important
+	%TODO give examples for various other methods in the area
+	
 	In order to make the computer react properly to your input, it needs to understand and therefore process the input in the first place. This can be achieved by the usage of some methods for natural language understanding, a subtopic of natural language processing.\cite{Wikipedi2013} There are various methods in this area but this paper utilizes two of them. The first one is the syntactic parsing, the second one the semantic analysis. To understand how these methods work, you need to know the basic terminology of the subject matter. In the following paragraphs the terms syntax, semantics and pragmatics are explained with respect to the two mentioned methods.
 	
 	The first method syntactic parsing relies on a grammar that describes the set of possible input, also called syntax. The syntax specifies what are allowed sentence structures and how these are built.
@@ -207,7 +210,7 @@
 	
 	The basic terminology should be clear by now. Whenever there are additional prerequisites to understand a method, these are explained in the section of that method.
 	
-	Before the actual evaluation of the starts, the usage of the result of both methods is shortly described. After both syntactic parsing and semantic analysis have been executed, in this order, you have a semantic representation of the input. This representation can be used  %TODO how can semantic representations be used
+	Before the actual evaluation of the methods starts, the usage of the result of both methods is shortly described. After both syntactic parsing and semantic analysis have been executed, in this order, you have a semantic representation of the input. This representation can be used  %TODO how can semantic representations be used
 	
 	In this paper both syntactic parsing and semantic analysis are presented. After the presentation of the methods, they are critically discussed to finally come to a conclusion.
 
@@ -342,13 +345,11 @@
 
 	Syntactic parsing is an important method on the way to understand natural language. The usage of dynamic programming algorithms circumvents many of the issues that classical top-down or bottom-up parsing algorithms face. Ambiguity is the most prominent of those issues. The best algorithm for context-free grammars is the CYK algorithm, which is a dynamic programming algorithm. But in practice it is very restricted, because it only works with grammars in CNF. But there are more complex dynamic programming algorithms that allow any kind of context-free grammar. Such an algorithm is the ``Earley Algorithm''\cite[p.~477]{Jurafsky2009b} which was already introduced in the critical discussion.
 	
-	Semantic analysis is the second method in the chain to understand natural language and therefore important as well. There are different approaches to the analysis. One of them is the syntax-driven approach that depends on parse trees. This dependency creates a delay effect: As long as a certain peace of text cannot be parsed, it definitely can't be analyzed for it's semantic meaning either. This is not an issue for restricted environments like programming languages or a very restricted subset of a natural language's grammar. But it is a major issue for real natural language, because there already the parsing does pose significant challenges.
+	Semantic analysis is the second method in the chain to understand natural language, as it is presented here, and therefore important as well. There are different approaches to the analysis. One of them is the syntax-driven approach that depends on parse trees. This dependency creates a delay effect: As long as a certain peace of text cannot be parsed, it definitely can't be analyzed for it's semantic meaning either. This is not an issue for restricted environments like programming languages or a very restricted subset of a natural language's grammar. But it is a major issue for real natural language, because there already the parsing does pose significant challenges.
 	
 	Looking into the future both methods require substantial improvements on the algorithm side to reach a point where understanding non-restricted natural languages becomes possible. But as it is right now it is not possible to create dialog systems that interact fully natural with humans. To make any kind of language interaction, the set of possible words and sentence structures must be restricted. But even if that is given (like in a flight check-in automaton), the computer has only a finite set of possible cases. The programmer can add tons of if-clauses or comparable statements to check for different cases but in the end it's all finite so that many of the user inputs must lead to the same output or no output at all. This fact has led to the current situation in which the most interaction with a computer happens via a restricted interface in which the user can only choose from a limited set of options (by clicking on a button, selecting an item of a list, etc.).
 	
-	%TODO the following paragraph is highly speculative, change	
-	
-	In addition the ambiguity of natural language is a major issue. Going back to the example in the introduction, the syntax-driven semantic analysis	does only work properly if the semantic meaning of the input has no ambiguity. But even than the generated meaning representation does not represent the pragmatic meaning. A dialog system is therefore far from being reached, because every input of a human can have dozens of different meanings. The intended meaning can sometimes depend on a thought that this human had while typing the input. As the computer doesn't have the ability to read thoughts, it would be impossible for the computer to determine the intended meaning of the input.
+	Furthermore the ambiguity of natural language is a major issue. The solution to it could lie in the understanding of the context. Even though natural language is full of ambiguity, we manage to communicate very successfully. Therefore the solution to ambiguity lies probably somewhere in our brain functionality. If we manage to find approaches that don't use traditional AI and First-Order logic but instead are inspired by our brain and try to understand and model natural language based on the context, we might as well have the solution to ambiguity altogether. But so far this is pure speculation and ambiguity remains a major issue.
 	
 	In a mission critical environment this ambiguity could lead to catastrophic results, because the computer, simply put, ``didn't get it''. This risk limits the usability of natural language communication with a computer for propably a long time to a very restricted set of use cases.