A Semantic Best-Effort Approach for Extracting Structured Discourse Graphs from Wikipedia

Digital Enterprise Research Institute www.deri.ie

A Semantic Best-Effort Approach for Extracting Structured

Discourse Graphs from WikipediaAndré Freitas, Danilo Carvalho, J. C. P. da

Silva, Sean O’Riain, Edward Curry

Outline

Motivation Representation

Requirements Semantic Best-effort Representation

Extraction Graphia Extractor Preliminary Evaluation Extraction Examples

Conclusion

Motivation

Linked Data Terminological and structural regularity Shared semantic agreement between data consumers

Natural language texts No terminological or structural

regularity Highly contextualized Complex semantic dependency

relations Ambiguity Information selection/normalization

- vocabulary constraints+ entity-centric+ pay-as-you-go data semantics = semantic best-effort

Motivation

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?

Motivational Scenario

What is the relationship between Barack Obama and Indonesia?

Sentence: From age six to ten, Obama attended local schools in Jakarta, including Besuki Public School and St Francis Assisi School.

Semantic Best-effort Extraction

Entity-centric text representation

Representation

Computational Linguistics Perspective

What is already there to represent NL? Discourse Representation Theory (DRT) Semantic Role Labeling (SRL)

Discourse Representation Theory (DRT) “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.

Semantic Role Labeling (SRL)

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.]

Semantic Best-Effort

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

Semantic Best-Effort Requirements

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)

Examples

- Text segmentation into (s,p,o)s

- Context representation- Resolve co-references (pay-as-you-go)- Conceptual model independency

Examples

- Context representation

Examples

- Represent recurrent discourse structures

Examples

- Resolve co-references (pay-as-you-go)

Examples

SDG Elements

Named, non-named entities and properties Quantifiers & operators Triple Trees Context elements Co-Referential elements Resolved & normalized entities

Graph Patterns

[[Interpretation]]

Graph traversal – deref sequence

Extraction

SBE Graph Extraction Tool

Extraction Pipeline Architecture

Subject Predicate Object Prepositional phrase & Noun complement Reification Time

Preliminary Evaluation

1033 relations (triples) from 150 sentences from 5 randomly selected Wikipedia articles

Manually classified the graphs: error categories and accuracy.

Preliminary Evaluation

Other Extraction Examples

Conclusion

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)

A Semantic Best-Effort Approach for Extracting Structured Discourse Graphs from Wikipedia

Documents

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