1 / 18

Language Technologies

Language Technologies. Reality and Promise in AKT Yorick Wilks and Fabio Ciravegna Department of Computer Science, University of Sheffield. HLT Using HLT for Knowledge Management Challenges for HLT in AKT Acquiring Knowledge Extracting Knowledge Publishing Knowledge Demos. Overview.

varick
Download Presentation

Language Technologies

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Language Technologies Reality and Promise in AKT Yorick Wilks and Fabio Ciravegna Department of Computer Science, University of Sheffield

  2. HLT Using HLT for Knowledge Management Challenges for HLT in AKT Acquiring Knowledge Extracting Knowledge Publishing Knowledge Demos Overview

  3. Goal Building systems able to process Natural Language in its written or spoken form Methodology Use of Language Analysis Technologies (examples): Information Extraction from Text Human-computer Conversation Machine Translation Text Generation Human Language Technology

  4. Use of HLT for Acquiring, Retrieving and Publishing Knowledge Expected main benefits Cost Reduction Time needed for KM Improving knowledge accessibility Accessing/Diffusing/Understanding Main challenges: User factor Integration HLT for KM in AKT

  5. HLT in AKT Knowledge acquisition retrieval publishing Text mining X InformationExtraction X X from Text Classification X X Summarization X Text Generation X Question X X Answering

  6. Drowning in information Starving for Knowledge Traditional Knowledge Management

  7. Direct access to knowledge when in textual format • Speed: Prompt Identification of critical factors • Quantity: more information can be accessed by people • Quality: only relevant information is accessed by people • Knowledge Sharing HLT Knowledge Management using HLT Information Extraction from Text Question Answering Text Summarization Reports written in natural language

  8. Acquisition • Document classification • Text mining Texts Populating with instances Publishing University of Sheffield • Document classification • Information Extraction • Document Generation & Summarisation • Agent Modelling Extraction Akt Challenges Ontologies

  9. Use of text mining for: Learning ontologies taxonomies Learning other relations Main challenges Integration of different techniques Keeping track of changing knowledge User factor: interaction for setup and validation HLT and KA in AKT

  10. Information Extraction from Text Populating ontologies with instances Information Extraction from Text Advantages: Direct access to knowledge when in textual format Speed: Prompt Identification of critical factors Quantity: more information can be accessed by people Quality: only relevant information is accessed by people Knowledge Sharing Knowledge extraction

  11. Question Answering Retrieving knowledge from repositories Question/Answering Advantage: Direct information access via Natural Language Knowledge Extraction (2) Q> How do you get a perfect sun tan? A> Lie in the sun NL-based Question NL Answer

  12. Adaptivity for new application definition Use of Machine Learning for new applications Moving new application building towards non experts Time reduction Criticality The user factor in training the system: What information/task can the user provide/perform for adapting the system? How can users know if the system does actually what required? The user factor

  13. Goal getting knowledge to the people who need it in a form that they can use. Means: Generation of texts from ontologies: Knowledge diffusion Knowledge documentation Text summarisation Generation of texts dependent on user knowledge state Publishing Knowledge

  14. Advantages: letting knowledge available: In the form needed by each user Expressed with the correct language type Expressed with the correct level of details Expressed without repetition of what is known. Skill reduction in querying ontologies Knowledge diffusion

  15. KM requires a number of HLT techniques to work together Complex tasks require complex interactions Integration is then a main issue How do you integrate the strength of each technology to build an effective system Working against current research paradigm HLT infrastructure

  16. HLT provides many (potential) benefits for KM Effectiveness Cost reduction Time reduction Subjectivity reduction KM provides many challenges for HLT User factors Integration Conclusions

  17. Amilcare: User-Driven Information Extraction from Text Future Technology Built in AKT Trestle Information Extraction Current Technology Demo

  18. Thank You!

More Related