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Human-human Communication and Knowledge Management

Human-human Communication and Knowledge Management. Kazuo Sumita Corporate R&D Center TOSHIBA Corp. Agenda. Toshiba’s NLP R&D activities Toshiba’s knowledge management system Shortage of the current KM system New approaches “GroupScribe” for group communication management

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Human-human Communication and Knowledge Management

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  1. Human-human Communication and Knowledge Management Kazuo Sumita Corporate R&D Center TOSHIBA Corp.

  2. Agenda • Toshiba’s NLP R&D activities • Toshiba’s knowledge management system • Shortage of the current KM system • New approaches • “GroupScribe” for group communication management • “MKIDS” for multi-modal knowledge sharing • Future work

  3. TOSHIBA NLP R&D activities 1/3 • Machine translation • English-Japanese MT :Dictionary tuning using large corpora • Chinese-Japanese MT :Hybrid framework using rule- and statistics-based approaches • English-Chinese MT :Prototyping using the framework for EJMT • Japanese-English speech translation : Prototyping • MT products TheHonyaku (PKG software), MT server (Japan Infoseek, lycos, excite, @nifty, Japan Patent Office’s IPDL, …), Engine License to other companies

  4. TOSHIBA NLP R&D activities 2/3 • Information retrieval and knowledge mining • Natural language based information retrieval • Question answering • Cross language information retrieval • Text mining: document clustering, categorization, information extraction • Knowledge mining products • KnowledgeMeister : KM software which can workwith several other systems (IBM WebSphere portal, Oracle9iAS Portal, Livelink, Fuji Xerox DocuCentre, Microsoft SharePointPortal server, Exchange, …) • NewsWatch : Information filtering of news articles

  5. TOSHIBA NLP R&D activities 3/3 • Speech processing • Speech synthesis: Provide human voice quality and naturalness, multi-lingual(American English, British English, Chinese, Dutch, French, German, Italian, Spanish and Japanese), small memory and low computational power • Robust speech recognition : High performance under noisy environments, multi-lingual • Japanese speech dictation : Speaker independent, high recognition rate without enrolment • Speech processing products • Middleware for car navigation systems, mobile equipments, game software, LaLaVoice(PKG software)

  6. Knowledge sharing system KnowledgeMeisterTM Chishiki-kyouyuu knowledge sharing • Features • NLP based information retrieval • Hierarchical clustering of accumulated documents • Categorizing newly input document • Various functions for knowledge sharing

  7. Knowledge sharing system Questioner Tell me how to write an equipment plan. Language/ intention understanding Information retrieval Office knowledge Intranet Personal know-how Experienced person

  8. Semantic Roles: Example 1/2 Examples of search requests from a knowledge sharing in TOSHIBA corporate R&D center (English translations) “When do we have to leave the dormitory?” “Who can apply for a child-care leave?” “Where can we have Chinese food in Kawasaki?” Extracted semantic roles : time, person, place

  9. Semantic Roles: Example 2/2 Example request from customer support (English translation) “I am a dynabook XX user. I’ve just pressed the power button without shutting it down.Now it displays an error message XXX”. Extracted semantic roles: BackgroundActionSymptom

  10. Insufficiency of the current KM systems Treatment of knowledge exchange in human-human communication • Knowledge and information exchanged by e-mail →GroupScribe • Multi-modal knowledge such as video and speech → MKIDS

  11. Convert human-human communication to sharable knowledge Sharable knowledge Communication Reusable knowledge E-mail Interactive e-Learning Multi-modal knowledge sharing F2F dialogue Education Office New communication

  12. CIKLE : Community Knowledge WareCommunity-based Interactive Knowledge Leveraging Environment • Drive communication-knowledge cycle • Extract and leverage knowledge from/through communication • Find and recommend knowledge to activate communication Knowledgebase (Stock-type) Flow-Stock combination structure Linkeddocument Messagethread Extract / Edit Extract and leverageknowledge Bind Find and recommendknowledge Comment / Post Communication (Flow-type)

  13. The CIKLE solution delivers… • Collaborative knowledge leveraging • Edit knowledge with a community consensus • Create knowledge with dialogue summarization engine • Publish sharable knowledge from even closed communities • Provide dual view: knowledge and its context • Retrieval of relevant knowledge in a natural way • Accept natural language queries • Give priority to documents than messages

  14. Information extraction (GroupScribe) Enhancement of the summarization function of CIKLE

  15. Rule based extraction • Surface expressions in each message • Reference relations between messages

  16. Knowledge sharing practice in TOSHIBA corp. CIKLE 07/2000 ～ 05/2003 CIKLEgs 05/2003 ～

  17. Multi-modal knowledge sharing system (MKIDS) Experienced person (Answerer) Questioner How should I do to manage the ×× when ○○ ? The way of managing ×× is… How should I do to manage the ×× when ○○ ? The way of managing ×× is … ① Question and Answer ② ③ Retrieval and reuse of the accumulated knowledge • Accumulation of the answering video image • Refinement of the knowledge Knowledge DB ○○を××する方法 カドの Ｒ=2-5 処理方 法… Authoring tool

  18. Multi-modal knowledge sharing Questioner’s side Answerer’s side

  19. Distribution QA retrieval System configuration Answerer side Media capture Questioner side Media capture Video dialogue Semantic role analysis Authoring tool Knowledge DB Native XML database

  20. Snapshot of a dialogue Questioner Answerer Ano kikitai no desuga(Ur, I have a question.) Hai nande shou (Hello! May I help you?) [2]question [2]nodding Semantic role analysis result Speech recognition result Ano hito no tamedakedo (For that person.) Haittande nao (Something entered.)

  21. Future work • Application of the systems to several real works and the evaluation • Improvement of the scalability and robustness • Adoption of more natural language techniques such as IE of named entities for generating effective summary

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