Paper angefangen auszuformulieren.

prosem/prosem-ki.bib

@@ -24,6 +24,89 @@
   timestamp = {2013.10.29}
 }
 
+@INBOOK{Jurafsky2009,
+  chapter = {18},
+  pages = {617--644},
+  title = {Speech and Language Processing},
+  publisher = {Pearson},
+  year = {2009},
+  author = {Jurafsky, Daniel and Martin, James H.},
+  series = {Prentice-Hall series in artificial intelligence},
+  edition = {Second},
+  abstract = {Sentences get their meanings from the words they contain and the syntactic
+	order of the words. Therefore the meaning of a sentence is partially
+	based on the words and its syntactic structure. The composition of
+	meaning representation is guided by the syntactic components and
+	relations provided by grammars such as CFGs.
+	
+	
+	A meaning representation is generated by first sending the input through
+	a parser which results in the syntactic analysis and second passing
+	this analysis as input to a semantic analyzer.
+	
+	
+	In the syntax-driven semantic analysis it is assumed that syntactic,
+	lexical and anaphoric ambiguities are not a problem.
+	
+	
+	The semantic meanings are attached to the grammar rules and lexical
+	entries from which trees are generated in the first place. This is
+	called rule-to-rule hypothesis.
+	
+	
+	The semantic attachments are written in braces after the syntactic
+	rules themselves.
+	
+	
+	After the syntactic analysis has been created, every word receives
+	a FOL predicate and/or term. The semantic analyzer goes the tree
+	up until the complete FOL term has been created. On the way lambda
+	reduction is used to replace predicates and terms with their proper
+	meanings, received from other parts of the tree.},
+  booktitle = {Speech and Language Processing},
+  owner = {jim},
+  quality = {1},
+  timestamp = {2013.11.16}
+}
+
+@INBOOK{Jurafsky2009a,
+  chapter = {17},
+  pages = {579--616},
+  title = {Speech and Language Processing},
+  publisher = {Pearson},
+  year = {2009},
+  author = {Jurafsky, Daniel and Martin, James H.},
+  series = {Prentice-Hall series in artificial intelligence},
+  edition = {Second},
+  abstract = {Lambda notation is used to bind variables dynamically to later appearing
+	contents.
+	
+	
+	lambda x P(x)(y) results in P(y) after a lambda reduction as x has
+	been bound to y.
+	
+	lambda P P(x)(lambda x Restaurant(x)) results in lambda x Restaurant(x)(x)
+	which results in Restaurant(x)},
+  booktitle = {Speech and Language Processing},
+  owner = {jim},
+  quality = {1},
+  timestamp = {2013.11.16}
+}
+
+@INBOOK{Jurafsky2009b,
+  chapter = {13},
+  pages = {461--492},
+  title = {Speech and Language Processing},
+  publisher = {Pearson},
+  year = {2009},
+  author = {Jurafsky, Daniel and Martin, James H.},
+  series = {Prentice-Hall series in artificial intelligence},
+  edition = {Second},
+  owner = {jim},
+  quality = {1},
+  timestamp = {2013.11.17}
+}
+
 @CONFERENCE{Kessler1997,
   author = {Kessler, Brett and Nunberg, Geoffrey and Schuetze, Hinrich},
   title = {Automatic Detection of Text Genre},
@@ -150,17 +233,14 @@
 }
 
 @INBOOK{Russel2010,
-  author = {Russel, Stuart J. and Norvig, Peter},
-  title = {Artificial intelligence: A Modern Approach},
-  booktitle = {Artificial intelligence: A Modern Approach},
-  year = {2009},
-  date = {December 11},
-  bookauthor = {Russel, Stuart J. and Norvig, Peter},
-  edition = {Third},
-  series = {Prentice-Hall series in artificial intelligence},
-  publisher = {Prentice Hall},
   chapter = {23},
-  pages = {888-927},
+  pages = {888--927},
+  title = {Artificial intelligence: A Modern Approach},
+  publisher = {Pearson},
+  year = {2009},
+  author = {Russel, Stuart J. and Norvig, Peter},
+  series = {Prentice-Hall series in artificial intelligence},
+  edition = {Third},
   abstract = {The first method to understanding natural language is syntactic analysis
 	or parsing. The goal is to find the phrase structure of a sequence
 	of words according to the rules of the applied grammar.
@@ -311,6 +391,9 @@
 	task against a restricted set of options. A general purpose system
 	can only work accurately if it creates one model for every speaker.
 	Prominent examples like Apple's siri are therefore not very accurate.},
+  bookauthor = {Russel, Stuart J. and Norvig, Peter},
+  booktitle = {Artificial intelligence: A Modern Approach},
+  date = {December 11},
   owner = {jim},
   timestamp = {2013.10.24}
 }
@@ -330,8 +413,8 @@
   title = {Dependency Parsing by Belief Propagation},
   booktitle = {Conference on Empirical Methods in Natural Language Processing},
   year = {2008},
-  date = {October 25 - October 27},
   pages = {145-156},
+  date = {October 25 - October 27},
   owner = {jim},
   quality = {1},
   timestamp = {2013.10.29}