Event-Extraction
Implementation of event extraction in "Event Representations for Automated Story Generation with Deep Neural Nets"
Following Pichotta and Mooney (2016a), we developed a
4-tuple event representation (s, v, o, m) where v is a verb, s
is the subject of the verb, o is the object of the verb, and m is
the modifier or “wildcard”, which can be a propositional object,
indirect object, causal complement (e.g., in “I was glad
that he drove,” “drove” is the causal complement to “glad.”),
or any other dependency unclassifiable to Stanford’s dependency
parser.
To generalize the content, the author uses Wordnet and Verbnet to replace the original word with higher level one.
Requirements
- python3.6
- nltk: wordnet, verbnet
- stanford core nlp jar files
To Do List
- Replcing Named Entities
- Genra Clustering