Most information extraction approaches available today have either focused on the extraction of simple relations or in scenarios where data extracted from texts should be normalized into a database schema or ontology. Some relevant information present in natural language texts, however, can be irregular, highly contextualized, with complex semantic dependency relations, poorly structured, and intrinsically ambiguous. These characteristics should also be supported by an information extraction approach. To cope with this scenario, this work introduces a seman- tic best-effort information extraction approach, which targets an information extraction scenario where text information is extracted under a pay-as-you-go data quality perspective, trading high-accuracy, schema consistency and terminological normalization for domain-independency, context capture, wider extraction scope and maximization of the text semantics extraction and representation. A semantic information ex- traction framework (Graphia) is implemented and evaluated over the Wikipedia corpus.

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A Semantic Best-Effort Approach for
Extracting Structured Discourse
Graphs from Wikipedia
André Freitas, Danilo Carvalho, J. C. P. da Silva,
Sean O’Riain, Edward Curry
© Copyright 2009 Digital Enterprise Research Institute. All rights reserved.

2. Outline
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 Motivation
 Representation
 Requirements
 Semantic Best-effort Representation
 Extraction
 Graphia Extractor
 Preliminary Evaluation
 Extraction Examples
 Conclusion

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Motivation

4. Motivation
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5. Motivation
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6. Motivation
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7. Motivation
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Natural language texts Linked Data
 No terminological or structural
regularity
 Terminological and
structural regularity
 Highly contextualized
 Shared semantic agreement
 Complex semantic dependency between data consumers
relations
 Ambiguity
Information selection/normalization
- vocabulary constraints
+ entity-centric
+ pay-as-you-go data semantics
= semantic best-effort

8. Motivation
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 Vocabulary-independent (schema-free queries)
 How to abstract users from knowing the data
representation?
 Semantic matching
 Schemaless databases in the limit demands
vocabulary-independency
 How information extraction is reshaped in this
scenario?

9. Motivational Scenario
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What is the relationship between Barack Obama and Indonesia?
Semantic Best-
effort Extraction
Sentence: From age six
to ten, Obama attended
local schools in Jakarta, Entity-centric text
including Besuki Public representation
School and St Francis
Assisi School.

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Representation

11. Computational Linguistics Perspective
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 What is already there to represent NL?
 Discourse Representation Theory (DRT)
 Semantic Role Labeling (SRL)

12. Discourse Representation Theory (DRT)
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 “The key idea behind (...) Discourse Representation Theory is
that each new sentence of a discourse is interpreted in the
context provided by the sentences preceding it.”
van Eijck and Kamp
 Models propositions in discourse (multiple sentences).
 Discourse representation structures (DRS).
John enters a card. Every card is green.

13. Semantic Role Labeling (SRL)
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 Shallow semantic parsing.
 Detection of arguments associated with a predicate.
 Associated semantic types to arguments.
Bill cut his hair with a razor
[Agent Bill] cut [Patient his hair] [Instrument with a razor.]

14. Semantic Best-Effort
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 Objectives:
 Entity-centric & Standardized: easier to integrate with
other resources
 Remove the formal constraints and the ‘baggage’ from
existing approaches
 Representation robust to extraction limitations/errors

15. Semantic Best-Effort Requirements
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 Text segmentation into (s,p,o)s
 Context representation
 Conceptual model independency
 Resolve co-references (pay-as-you-go)
 Represent recurrent discourse structures
 Standardized representation (RDF(S))
 Principled interpretation (compositionality)

16. Examples
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- Text segmentation into (s,p,o)s
- Context representation
- Resolve co-references (pay-as-you-go)
- Conceptual model independency

17. Examples
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- Context representation

18. Examples
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19. Examples
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- Represent recurrent discourse structures

20. Examples
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- Represent recurrent discourse structures
- Resolve co-references (pay-as-you-go)

21. Examples
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- Represent recurrent discourse structures

22. SDG Elements
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 Named, non-named entities and properties
 Quantifiers & operators
 Triple Trees
 Context elements
 Co-Referential elements
 Resolved & normalized entities

23. Graph Patterns
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24. [[Interpretation]]
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 Graph traversal – deref sequence

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Extraction

26. SBE Graph Extraction Tool
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27. Extraction Pipeline Architecture
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 Subject
 Predicate
 Object
 Prepositional phrase & Noun complement
 Reification
 Time

28. Preliminary Evaluation
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 1033 relations (triples) from 150 sentences from 5
randomly selected Wikipedia articles
 Manually classified the graphs: error categories
and accuracy.

29. Preliminary Evaluation
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30. Other Extraction Examples
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31. Other Extraction Examples
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32. Other Extraction Examples
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33. Other Extraction Examples
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34. Other Extraction Examples
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35. Other Extraction Examples
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36. Other Extraction Examples
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37. Other Extraction Examples
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38. Other Extraction Examples
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39. Other Extraction Examples
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40. Other Extraction Examples
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41. Other Extraction Examples
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42. Conclusion
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 Main direction for improvement is completeness
 Aligned with the pay-as-you-go scenario
 Still need to define clear criteria for what you can’t extract
 There is still a long way to go (e.g. complex subordination)
 Investigation using existing n-ary relations patterns
 Context (reification) should be a first-class citizen in the
representation of natural language
 Focus on getting the semantic pivots (rigid designators) right
 Worth putting effort on enumerable patterns (timestamps,
operators)