Additional points added

Author: Vineet John

project-report/cs698_project_report.tex

@@ -78,9 +78,6 @@ \section{Goal} % (fold)
 % section goal (end)
 
 
-\newpage
-
-
 \section{A Primer of Neural Net Models for NLP} % (fold)
 \label{sec:a_primer_of_neural_net_models_for_nlp}
 
@@ -111,6 +108,7 @@ \section{A Primer of Neural Net Models for NLP} % (fold)
 \section{A Neural Probabilistic Language Model} % (fold)
 \label{sec:a_neural_probabilistic_language_model}
 
+  \textbf{Background:}
   \begin{itemize}
     \item 
     We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences.
@@ -127,9 +125,16 @@ \section{A Neural Probabilistic Language Model} % (fold)
     \item 
     This will ensure that semantically similar words end up with an almost equivalent feature vectors, called learned distributed feature vectors.
     \item 
-    
+    A challenge with modeling discrete variables like a sentence structure as opposed to a continuous value is that the continuous valued function can be assumed to have some form of locality, but the same assumption cannot be made in case of discrete functions.
+    \item 
+    N-gram models try to acheive a statistical modeling of languages by calculating the conditional probabilities of each possible word that can follow a set of $n$ preceding words.
+    \item 
+    New sequences of words can be generated by effectively gluing together the popular combinations i.e. n-grams with very high frequency counts.
   \end{itemize}
 
+  \textbf{Goal of the paper:}
+  Knowing the basic structure of a sentence, we should be able to create a new sentence by replacing parts of the old sentence with interchangeable elements.
+
 % section a_neural_probabilistic_language_model (end)