1 dr alexiei dingli introduction to web science reusing knowledge
Post on 21-Dec-2015
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Dr Alexiei Dingli
Introduction to Web Science
Reusing knowledge
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• Acquire
• Model
• Reuse
• Retrieve
• Publish
• Maintain
Six challenges of the Knowledge Life Cycle
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• Three reusable types of objects
– Ontologies
– Problem Solving Methods
– Knowledge Bases
• Plus we can also use additional sources (WWW)
Reusing knowledge
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• Locating the knowledge to be reused is difficult
• Distributed agents may be unaware that the knowledge they need is available (this is the challenge of knowledge retrieval)
• Knowledge may simply be in the wrong form for the task
Problems with reuse
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• Question answering
• Dialogue systems
Two particular reuse tasks
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• The main aim of QA is to present the user with a short answer to a question rather than a list of possibly relevant documents.
• As it becomes more and more difficult to find answers on the WWW using standard search engines, question answering technology will become increasingly important.
What is Question Answering?
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• Clearly there are many different types of questions:
– When was Mozart born?• Question requires a single fact as an answer.• Answer may be found verbatim in text i.e. “Mozart
was born in 1756”.
– How did Socrates die?• Finding an answer may require reasoning.• In this example die has to be linked with drinking
poisoned wine.
Question Types (1)
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– How do I assemble a bike?• The full answer may require fusing information from many
different sources.• The complexity can range from simple lists to script-based
answers.
– Is the Earth flat?• Requires a simple yes/no answer.
Question Types (2)
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• The biggest independent evaluations of question answering systems have been carried out at TREC (Text Retrieval Conference)
– Five hundred factoid questions are provided and the groups taking part have a week in which to process the questions and return one answer per question.
– No changes are allowed to your system between the time you receive the questions and the time you submit the answers.
Evaluating QA Systems
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A Generic QA Framework
DocumentCollection
Search EngineTop n
documents
DocumentProcessing
Questions Questions
Answers
• A search engine is used to find the n most relevant documents in the document collection
• These documents are then processed with respect to the question to produce a set of answers which are passed back to the user
• Most of the differences between question answering systems are centred around the document processing stage
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• The answers to the majority of factoid questions are easily recognised named entities, such as countries, cities, dates, peoples names, etc
• The relatively simple techniques of gazetteer lists and named entity recognisers allow us to locate these entities within the relevant documents – the most frequent of which can be returned as the answer
• This leaves just one issue that needs solving – how do we know, for a specific question, what the type of the answer should be
A Simplified Approach
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• The simplest way to determine the expected type of an answer is to look at the words which make up the question:
• who – suggests a person• when – suggests a date• where – suggests a location
A Simplified Approach (1)
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• Clearly this division does not account for every question but it is easy to add more complex rules:
• country – suggests a location
• how much – suggests an amount of money
• author – suggests a person
• birthday – suggests a date
• college – suggests an organization
• These rules can be easily extended as we think of more questions to ask
A Simplified Approach (2)
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• The most frequently occurring instance of the right type might not be the correct answer.
– For example if you are asking when someone was born, it maybe that their death was more notable and hence will appear more often (e.g. John F Kennedy’s assassination).
• There are many questions for which correct answers are not named entities:
– How did Ayrton Senna die? – in a car crash
Problems (1)
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• The gazetteer lists and named entity recognisers are unlikely to cover every type of named entity that may be asked about:
– Even those types that are covered may well not be complete.
– It is of course relatively easy to build new lists, e.g. Birthstones.
Problems (2)
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• Amber• Precious• Diamond• Asia• Summer• Holly
• Are these person’s names?
Does a gazetteer of people names contains all the names?
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• A sequence of utterances• Exchange of information among multiple
dialogue participants• Stays coherent over the time• Driven by certain goal
– finding the most suitable restaurant in a foreign city,
– booking the cheapest flight to a given city,
– controlling the state of the devices in a home,
– or the goal might also be the interaction itself (chatting)
Dialogue (1)
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• Most natural means for communication for humans perceived as a very expressive, efficient and robust
• However, dialogue is very complex protocol– follow certain conventions or protocols that are adopted by
participants
– humans usually use their extensive knowledge and reasoning capabilities to understand the conversational partner
– the dialogue utterances are often imperfect – ungrammatical or elliptical
Dialogue (2)
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• People often utter partial phrases to avoid repetition– A: At what time is “Titanic” playing?– B: 8pm– A: And “The 5th element”?
• It is necessary to keep track of the conversation to complete such phrases
Ellipsis
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• Some words can only be interpreted in context:– Previous context (anaphora)
• “The monkey took the banana and ate it”
– Future context (cataphora)• “Give me that. The book by the lamp.”
– Temporal/spatial• “The man behind me will be dead
tomorrow.”• (Who is the man? When he died/dies?)
Deixis
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• The meaning of a discourse may be far from literal.– B: I can’t reach him.– A: There is the telephone.– B: I am not in my office.– A: Okay.
• Undertones & implications are often employed for effect or efficiency
Indirect Meaning
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• People seem to know very well when they can take their turn– There is little overlap (5%)– Gaps are often a few 1/10ths of a second– Appears fluid, but not obvious why
• A computational model of overlap does not exists– causes problem for dialogue systems
Turn Taking
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• Phrases like “a-ha”, “yes”, “hmm” or “eh” are often prompted in order to fill the pauses of the conversation, to indicate the attention or reflection
• The challenge here is to recognize when they should be understood as a request for turn taking and when they should be ignored
Conversational fillers
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• Flight and train timetable information and reservation
• Smart homes
• Automated directory enquires– Yellow pages enquires– Weather information
Most common dialogue domain
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Components of a Dialogue System
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• Transforms speech to text
• Two basic types– Grammar-based ASR
• The set of accepted phrases defined by regular/context-free grammars (i.e. language model in the form of a grammar)
• Usually speaker independent
– Dictation machine• Recognizes “any utterance”• N-gram language model• Often speaker dependent
Automatic Speech Recognition
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• Analyzes textual utterance and returns its formal semantic representation– Logical formula– Named entities– etc
Natural Language Understanding
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• Coordinates activity of all components
• Maintains representation of the current state of the dialogue
• Communicates with external applications
• Decides about the next dialogue step
Dialogue Manager
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• Finite-state– dialogue flow determined by a finite state automata
• Frame-based– form filling
• Plan (task) based– a dynamic plan is constructed to reach the dialogue goal
• … in practice, you often find an extended versions or combinations of above mentioned approaches!
Three types of DM
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Finite State Automata
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Frame Based
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• Take a problem solving approach– There are goals to be reached– Plans are made to reach those goals– The goals and plans of the other participants must be
iteratively inferred or predicted
• Potential for handling complicated dialogues– suffers from today’s technological limitation– in more complex cases the planning problem can become
computationally intractable
• Examples: Bathroom consultant
Plan Based
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• Produces a textual utterance (so called surface realization) from an internal (formal) representation of the answer
• The surface realization can include formatting information– Speaking style, pauses– Background sounds
Natural Language Generation
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• Transforms the surface realization into a an acoustic representation (sound signal)
Text-To-Speech
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• Commercial systems:– small vocabulary (~100 words)– closed domain– system initiative
• Research systems:– larger (but still small) vocabulary (~10000
words)– closed domain– (limited) mixed initiative
Typical parameters
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• System-initiative– system always has control, user only responds to
system questions
• User-initiative: – user always has control, system passively answers
user questions
• Mixed-initiative: – control switches between system and user using
fixed rules
• Variable-initiative: – control switches between system and user
dynamically based on participant roles, dialogue history, etc.
Different Initiatives
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• Several possible input/output modalities to communicate with dialogue systems
– speech, text, pointing, graphics, gestures, face configurations, body positions, emotions, etc.
• Not single “most convenient” modality (different modalities have different advantages)
– entering day of week: click on a calendar– entering Zip code: use keyboard– performing commands: speech– complex query: express them as typed natural language
• Several modalities useful– when one modality is not applicable - e.g. eyes or hands are busy,
silent environment– or when difficult to use - e.g. small devices with limited keyboard and
small screen
Multi Modal Dialogue Systems
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• Comic
• Companions
Case Study
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The Comic Avatar
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Wizard of Oz
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Putting it together
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The Companions Architecture
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The Companions Robot
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The Companions Interface 1
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The Companions Interface 2
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Questions?