1. A Database of Narrative Schemas A 2010 paper by Nathaniel Chambers and Dan Jurafsky Presentation by Julia Kelly

2. Natural Language Understanding (NLU) A more specific definition of a sub-topic for Natural Language Processing (NLP) The Parsing rather than the generating part of NLP

3. Chambers and Jurafsky: What are they trying to solve? Using unsupervised learning (machine learning technique) in conjunction with coreference chains to extract rich event structure in order to produce better narrative schema

4. What came before? FrameNet: Baker (also a Upenn affiliate) et al FrameNet: Baker (also a Upenn affiliate) et al – 1997 1997 – Frame definition Frame definition – Annotation Annotation TimeBank and TempEval : Pustejovsky et al TimeBank and TempEval : Pustejovsky et al – TimeBank is a corpus (often referred to as TimeML now) TimeBank is a corpus (often referred to as TimeML now) – TempEval seems to organize by events TempEval seems to organize by events

6. TimeBank TimeML is a robust specification language for events and temporal expressions in natural language. It is designed to address four problems in event and temporal expression markup: Time stamping of events (identifying an event and anchoring it in time); Ordering events with respect to one another (lexical versus discourse properties of ordering); Reasoning with contextually underspecified temporal expressions (temporal functions such as 'last week' and 'two weeks before'); Reasoning about the persistence of events (how long does an event or the outcome of an event last).

7. TASK A: For a restricted set of event terms, identify temporal relations between events and all time expressions appearing in the same sentence. (NOTE: The restricted set of event terms is to be specified by providing a list of root forms. Time expressions will be annotated in the source, in accordance with TIMEX3.) TASK B: For a restricted set of event terms, identify temporal relations between events and the Document Creation Time (DCT). (NOTE: The restricted set of events will be the same as for Task A. DCTs will be explicitly annotated in the source.) TASK C: Identify the temporal relations betweeen contiguous pairs of matrix verbs. (NOTE: matrix verbs, i.e. the main verb of the matrix clause in each sentence, will be explicitly annotated in the source.) We specify three separate tasks that involve identifying event-time and event-event temporal relations. A restricted set of temporal relations will be used, which includes only the relations: BEFORE, AFTER, and OVERLAP (defined to encompass all cases where event intervals have non-empty overlap).

8. Chambers and Jurafsky: Narrative Schema Narrative Schema contain: Narrative Schema – Sets of related events – A temporal ordering of events – The semantic roles of the participants Based off of scripts.

9. Narrative Schema A brief example Hand-selected examples from the Database

10. Chambers and Jurafsky: A better approach A schema is not just a chain of synonyms Counting times verbs appear before or after on another Combines both the schema and temporal data within the database

11. Narrative Coherence Assumption Predicates sharing coreferring arguments are related by virtue of narrative discourse structure. In order to find events, 1. Parse all sentences into dependency graphs. 2. Run coreference over the document. 3. Count all pairs of verbs and dependencies (e.g. subject, object) that are filled by coreferring entities. 4. Record with each pair the head word of the shared argument.

12. Why did other approaches not make the grade? FrameNet approaches the problem from the aspect of frames TimeBank did not also include a method of schema in addition to temporal placement

13. Performance The Chambers and Jurafsky database has very similar results to the hand-labeled FrameNet data. The temporal aspect of the database did not seem to perform as well as the TimeBank Somewhat specialized