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Some Thoughts on HPC in Natural Language Engineering. Steven Bird University of Melbourne & University of Pennsylvania. Sponsorship. Natural Language Engineering: Integrating Parallel and Parametric Processing Victorian Partnership for Advanced Computing Expertise Grant EPPNME092.2003.

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some thoughts on hpc in natural language engineering

Some Thoughts on HPC inNatural Language Engineering

Steven Bird

University of Melbourne &

University of Pennsylvania


Natural Language Engineering: Integrating Parallel and Parametric Processing

Victorian Partnership for Advanced Computing Expertise Grant EPPNME092.2003

nle application areas
NLE Application Areas
  • Spoken dialogue systems
  • Cross-language information retrieval
  • Word-sense disambiguation
  • Multi-document summarisation
  • Natural language database interfaces
  • Information Extraction
  • Information Retrieval
  • Authoring Tools
  • Language Analysis
  • Language Understanding
  • Knowledge Representation
  • Knowledge Discovery
  • Spoken Language Input
  • Written Language Input
  • Natural Language Generation
  • Spoken Output
  • Multilinguality
  • Multimodality
  • Discourse and Dialogue
some nle applications in detail
Some NLE Applications in detail
  • Information extraction from broadcast news
    • Tokenization, alignment, entity detection, coreference resolution, semantic mapping
  • Spoken language dialogue systems (SLDS)
    • Speech recognition, parsing, user modelling, discourse management, generation, synthesis
  • Language analysis
    • Interlinear text annotation, lexicon development, morphosyntactic grammar development
meta activities
Meta Activities
  • Discovery
    • What tools work with data in format X?
    • What lexical resources exist for language Y?
  • Reuse
    • Diverse implementation frameworks
    • Component integration, wrapping, etc
  • Training and evaluation
    • Parametric and parallel processing
    • Comparing systems running on the same data
    • Gold standard vs theory comparison
    • Analyzing interaction logs
learn about nle
Learn about NLE
  • This department hosts a mirror of the ACL digital anthology
  • 50k pages, 40 years
  • Common components, different arrangements
    • Multiple components for doing the same task
  • Most NLE components convert between information types
    • Parser: from strings to trees
    • ASR: from speech to text
    • Summariser: from text to selected text
  • But:
    • Many processes benefit from other information sources (e.g. exploiting intonation in input)
    • Input and output can be aligned
    • Solution: multilayer annotations
annotation graphs
Annotation Graphs
  • Labelled digraphs with timestamped nodes
annotation graphs complex example
Annotation Graphs: complex example
  • AGTK: Annotation Graph Toolkit
    • library, applications
nle and grids
NLE and Grids
  • NLE Applications
    • typically constructed out of numerous components
    • each component responsible for a specialised task
    • executed against large data sets
  • To use grids in NLE:
    • subscribe to a model which allows automated discovery of data and components
    • flexible design of applications, coordination of execution, storage of results
  • Ideally:
    • view grid as a commodity, hidden from application developers
architectural components
Architectural Components
  • Data
    • Language resources for analysis
    • E.g. Switchboard, 2400 annotated telephone conversations (26 CDs)
  • Software Components
    • minimal individual functional units
      • e.g. Annotation Server, Alignment, ASR, Data Source Packaging, Format Conversion, Text Annotation, Lexicon Server, Semantic Mapping
    • common interface specification
  • Metadata Repositories
    • Dublin Core Application Profile for NLE resources
  • Application
    • data + components + processing instructions
    • declarative specification in XML
  • Grid Service
    • computational and storage resources for application execution
  • Natural Language Engineering
    • interesting test case for grid services
    • many mature component technologies
    • applications that are both data and processor intensive
    • applications for building the multilingual information society of the future...