Fixed up some citations

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@590 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 3 insertions, 3 deletions

diff --git a/report/setup.tex b/report/setup.tex
index 8fccf1b3..b4f3f07d 100644
--- a/report/setup.tex
+++ b/report/setup.tex
@@ -1,8 +1,8 @@
 \chapter{Experimental Setup}
 
-Our approach is based upon the popular and influential Hiero system \cite{chiang07} which uses a synchronous context free grammar (SCFG) to model translation. 
+Our approach is based upon the popular and influential Hiero system \citep{chiang:2007} which uses a synchronous context free grammar (SCFG) to model translation. 
 This translation system uses only a single non-terminal symbol and therefore the system is inherently stateless. 
-However, we know that using a richer set of non-terminals can greatly improve translation, as evidenced by the improvments obtained by SAMT system \cite{samt} which augments a Hiero-style SCFG model with syntactic labels.
+However, we know that using a richer set of non-terminals can greatly improve translation, as evidenced by the improvments obtained by SAMT system \citep{samt} which augments a Hiero-style SCFG model with syntactic labels.
 This is best explained in terms of the generalisation capability: a single category grammar can create all manner of string-pairs, the majority of which are nonsensical and agrammatical, while a model with syntactic categories inherently limits the sets of string pairs to those which are grammatical (largely).
 This can be seen from the following example rules, showing how rules can be combined to arrive at ungrammatical string pairs.
 \begin{align}
@@ -24,7 +24,7 @@ This allows our approach to be more easily ported to work for translating a vari
 \section{Distributional Hypothesis}
 
 Underlying most models of grammar induction is the distributional hypothesis. This theory states that
-``words that occur in the same contexts tend to have similar meaning'' \cite{harris:54}. Although phrased in terms of semantics, the distributional hypothesis applies equally to syntax, that is, words that can be substituted for one another most often share the same syntactic category (in general, semantics implies syntax). This is evidenced by the wide-spread use of the substitution test in theories of syntax to determine the constituency and syntactic category of a word or phrase.
+``words that occur in the same contexts tend to have similar meaning'' \citep{harris:54}. Although phrased in terms of semantics, the distributional hypothesis applies equally to syntax, that is, words that can be substituted for one another most often share the same syntactic category (in general, semantics implies syntax). This is evidenced by the wide-spread use of the substitution test in theories of syntax to determine the constituency and syntactic category of a word or phrase.
 
 The majority of work on monolingual grammar induction has used some notion of context to inform the induced categories. This is best seen in the work of Alex Clark who uses the context surrounding a phrase to determine its category, and in Dan Klein's work, which uses context to determine constituency. In this project we follow the lead of these earlier works on monolingual grammar induction by using context to inform our clustering of words and phrases, such that words that appear in similar contexts are assigned to the same cluster. We expect that this clustering should bear a strong resemblance to the underlying syntax and, to some extent, the semantics of the language, and therefore improve translation accuracy.