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\section{Extraction Templates}
To extract event relations, different types of concepts need to be identified. These are listed in Table 2. The first four relations, namely \textit{EffectOf}, \textit{EffectOfIsState}, \textit{EventForGoalEvent} and \textit{EventForGoalState} are similar to those used in ConceptNet to describe events. \textit{Happens(f, t)} represents that a fluent \textit{f} holds at time \textit{t}. Fluent is a concept adopted from (cite) and is considered as an event in our research. The last event relation, \textit{CauseOfIsState}, is derived from the first two event relations, and is used to represent the state that a story character is in that may lead to the execution of an event. For example, \textit{CauseOfIsState(sleep, tired)} means that if a story character is \textit{tired} (a state), he/she may \textit{go to sleep} (an event).
Table 2. Concepts in Eventure
These concepts are used to define the elements that comprise an extraction template, which are shown in Table 3. The last two elements are based from McIntyre and Lapata's (cite) content planning phase for ?story generation?, and are used in Eventure to extract event relations that span across sentences.
Table 3. Elements used in Extraction Templates
\subsection{Extraction Templates}
12 extraction templates for event relations were defined. These templates are shown in Table 4.
Table 4. Extraction Templates for Event Relations
\subsection{Extraction Rules}
Each of the extraction templates has an associated set of rules (why?).
Summarize the types of rules and provide one or two example...
\subsection{Pre-Processing}
The corpus is comprised of 30 stories for children age five to eight that were collected from the Internet and manually pre-processed to reformat dialogues into direct sentences; and to split compound, complex and compound-complex sentences to simple sentences. The corpus is then passed to automated pre-processing for POS tagging, tokenization and co-reference resolutions are then applied, yielding the sample output in Listing 1:
Listing 1. Sample output from pre-processing module
Sample Sentence: Piglet smiled because Tigger gave him a present
Tokenizer, POS Tagger: [Piglet] [smiled] [because] [Tigger] [gave] [him] [a] [present][.]
Gazetteer: [character] [taskindicator, causeindicator] [character] [determiner]
Morphological Analyzer: [smile] [give]
Chunker: [B-NP] [B-VP] [B-SBAR] [B-NP] [B-VP] [B-NP] [I-NP] [I-NP][O]
