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The Loose Speak Project James Fan Knowledge-Based Systems Research Group University of Texas at Austin May 8, 2002

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The Loose Speak ProjectJames FanKnowledge-Based Systems Research GroupUniversity of Texas at AustinMay 8, 2002

Outline

- Loose speak overview
- Two types of LS:
- Complex nominal interpretation
- Metonymy
- Future work on LS

Loose Speak

- Loose speak(LS) : the phenomenon that human listeners are able to correctly interpret a speaker's imprecise utterance.
- Relevance:
- LS common in human communication, but rarely supported in human-computer-interaction.
- Lack of LS requires SMEs talk in precise terms to KB, makes KA tedious and error prone, and contributes to the brittleness of KB.

Here's a KA example without LS ...

Loose Speak Project

- Task: given an imprecise expression, correctly form an interpretation consistent with the existing KB.
- Goal:
- To show that (semi)-automatic interpretation of imprecise terms is possible.
- It can accelerate KA.

Here's how the previous example works with LS ...

KA With LS

After clicking on the “See CMAP” link …

Outline

- Loose speak overview
- Two types of LS:
- Complex nominal interpretation
- Metonymy
- Future work on LS

Complex Nominal Interpretations

- Complex nominal is an expression that has a head noun proceeded by a modifying element. [Levi '78] the semantic relation between the two is implicit.
- Marble statue = statue made of marble
- Animal cell = cell that is the basic structural unit of an animal
- Metal detection = detecting metal.
- Complex nominal interpretation task: given a complex nominal, return the semantic relation between the head noun and it's modifying element.

Related Work

- A set of rules that includes most of the semantic relations in complex nominals [Levi '78].
- Hand coded rules [Leonard '84].
- Statistically learned rules [Lauer '95].
- Learned rules under the user's guidance [Barker '98].

Our Approach

- Given a complex nominal made of two concepts H & M,
- Search KB up to certain depth, return any relations between H & any of M's super/subclasses, or vice-versa.
- If no relation is returned, select from a set of templates based on domain/range match.

Let's see what the templates are ...

Templates

- Templates: A set of 32 relations, which includes most of the common semantic relations occur in complex nominals.
- Example:
- (a H with (element-type ((a M))))
- (a H with (is-part-of ((a M))))
- ...
- Zero, one, or multiple relations may be returned.

Let's step through a few examples ...

M = Animal & H = CellExample 1

- Given a complex nominal made of two concepts H & M,
- Search KB up to certain depth, return any relations between H & any of M's super/subclasses, or vice-versa.
- If no relation is returned, select from a set of templates based on domain/range match.

- Do breadth-first KB search, and found the following in KB:

- (every Cell has
- (is-basic-structural-unit-of ((a Organism)))

- Return:

- (a Cell with
- (is-basic-structural-unit-of ((a Animal)))

M = Cell & H = LocomotionExample 2

- Given a complex nominal made of two concepts H & M,
- Search KB up to certain depth, return any relations between H & any of M's super/subclasses, or vice-versa.
- If no relation is returned, select from a set of templates based on domain/range match.

- Do breadth-first KB search, and found the following in KB:

- (every Locomotion has
- (object ((a Tangible-Entity))))

- Return:

- (a Locomotion has
- (object ((a Cell))))

M = Bond & H = Energy.Example 3

- Given a complex nominal made of two concepts H & M,
- If no relation is returned, select from a set of templates based on domain/range match.

- Do breadth-first KB search, and found the nothing in KB:

- Select from the templates:

- (a Create with (raw-material ((a Bond))) (result ((a Energy)))) -- match.

- (a Create with (result ((a Bond))) (agent ((a Energy))) -- match.

- (a Energy with (element-type ((a Bond)))) -- mismatch.

... ...

Performance Measurements

- Precision = C / A, where C = number of instances in which a correct answer is returned, A = number of instances in which an answer is returned.
- Recall = C / T, where C = number of instances in which a correct answer is returned, T = the total number of test instances.
- Avg. ans. length = L / A, where L = total lengths of all the answers returned, A = number of instances in which an answer is returned.

Evaluation

- Tested on 2 sets of data from Alberts [Alberts, el] with a total of 184 test examples.
- Our approach has similar precision and recall values as the templates method does.
- Our approach has much shorter average answer length.
- The distribution of answer lengths is bi-modal: 65% answers have 1 or 2 choices; 19% have 9 or 10 choices.

Evaluation (Continued)

- Our approach is compared to a templates based method because the templates resemble the hand-coded rules approach.
- Mistakes from data set 2 are caused by
- invalid data entries (e.g. phosphate residue -> phosphate substance translation)
- incomplete KB (e.g. topic slot missing from KB).

Future Work for Complex Nominal Interpretation

- Gather more data for further evaluation.
- Integrate the KB search with the templates.

KB Search And Templates Integration

- KB search is bounded by a certain depth.
- The selections from the templates can direct deeper searches.
- Example:
- Cell Ribosome.
- KB search: found nothing.
- Templates:
- (a Ribosome with (is-part-of ((a Cell))))
- (a Ribosome with (material ((a Cell))))
- … …
- Deeper search reveals:
- (a Ribosome with (element-type-of ((a Aggregate with (is-part-of ((a Cytoplasm with (is-part-of ((a Cell))))))))))

Outline

- Loose Speak Overview
- Two types of LS:
- Complex Nominal interpretation
- Metonymy
- Future work on LS

Metonymy

- Metonymy: a figurative speech in which "one entity [the metonym] is used to refer to another [the referent] that is related to it". [Lakoff & Johnson '80]
- Example:
- Joe read Shakespeare. It was good.
- Metonymy resolution task: given an input expression denoting a piece of knowledge, identify any occurrence of metonymy, uncover the referent, and returned the paraphrased version of the input expression.

Traditional Approaches [Fass '91][Hobbs '93][Markert & Hahn '97][Harabagiu '98]

- Given an input (often in the form of sentences in natural language):
- Detect metonymy based on detection of type constraints,
- Resolve metonymy based on a search in metonymy space.
- Anaphora is used to validate the result of the metonymy resolution.

Let's what the metonymy space is ...

Metonymy Space

- Metonymy space: the set of entities related to the metonym.
- Metonymy space construction:
- given the metonym A, return set S = {X | exists A-r1-A1-r2-A2- ...-rn-X} where r1, r2, ..., rn are members of a fixed set of slots, such as has-part, material, agent, result, etc., and A1, A2, ..., X are frames.
- Given A = Shakespeare, S = {Shakespeare, His Head, His Text, ...} because
- Shakespeare, r1= self
- Shakespeare-has-part-His Head, r1= has-part
- Shakespeare-agent-of-Write-result-His Text, r1 = agent-of, A1= Write, r2= result

Let's step through a few examples ...

Given: “Joe read Shakespeare. It was good.”Metonymy Example 1 (Traditional Approach)

- Given an input (often in the form of sentences in natural language):
- Detect metonymy based on detection of type constraints,
- Resolve metonymy based on a search in metonymy space.
- Anaphora is used to validate the result of the metonymy resolution.

- Type constraints:
- agent-of-read: Person.
- object-of-read: Text

- MetonymySpace = {Shakespeare, His Head, His Text ... }.
- Selects His Text

- Anaphora It confirms His Text fits better than Shakespeare.

Given: “electrons are removed from water molecules.”Metonymy Example 2 (Traditional Approach)

- Given an input (often in the form of sentences in natural language):
- Detect metonymy based on detection of type constraints,
- Resolve metonymy based on a search in metonymy space.
- Anaphora is used to validate the result of the metonymy resolution.

- Type Constraints:
- object-of-remove: Entity.
- base-of-remove: Entity.

- No violation found, no metonymy resolution needed.

Metonymy Example 2 (Continued)

- However the input, “Electrons are removed from water molecules”, does need metonymy resolution in our representation because:
- Remove requires the base have the object as its part, e.g. water molecule should have a part called electron.
- Water molecule does not have a part called electron. It has a hydrogen atom part, which has a electron part, and it has an oxygen atom part, which has a electron part.
- Therefore the literal translation of the input does NOT work, and the traditional approach does NOT give the correct answer either.

Given (a Read with (object (Shakespeare))), translate it into: Read-object-ShakespeareOur Approach

- Given KM expression that can be translated into X-r-Y, where X, Y are frames, and r is a slot:

Do

X' = X, Y' = Y

I = Ideal(X, r)

M = MetonymySpace(Y)

Y = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

I = Ideal(Y, rinverse)

M = MetonymySpace(X)

X = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

Until (X = X' and Y = Y')

Return X'-r-Y'

- 1st iteration
- X’ = Read, Y’ = Shakespeare

- I = (a Text with (purpose ((a Role with (in-event ((a Read))))))
- M = {Shakespeare, His Head, His Text, …}
- Y = His Text
- I = (a Event)
- M = {Read}
- X = Read

2nd iteration

…

ReturnRead-object-His Text

Ideal and Metonymy Space

- Ideal:
- Type constraints.
- Add/delete/precondition list of the action.
- Teleological constraints.
- Metonymy Space: given the metonym A, return set S: {X | exists A-r1-A1-r2-A2- … -rn-X} where r1, r2, … , rn are members of a fixed set of slots, such as has-part, material, agent, result, etc., and A1, A2, … , X are frames.
- Search depth: the n in the A-r1-A1-r2-A2- … -rn-X path mentioned above. For example, search depth of A = 0, search depth of A1= 1, etc.

Distance Measurement and Comparison

- Distance - (p, n, t): the similarity between an element from the metonymy space and the ideal.
- p: number of shared properties between the element and the ideal.
- n: search depth of the element in the metonymy space.
- t: taxonomical distance between the element and the ideal.
- Given (p1, n1, t1) and (p2, n2, t2), then.
- (p1, n1, t1) < (p2, n2, t2) if:
- p1 > p2 or.
- p1 = p2 and n1 < n2 or.
- p1 = p2 and n1 = n2 and t1 < t2.

Given (a Remove with (object ((a Electron))) (base ((a Water-Molecule)))), translate into Remove-object-Electron and Remove-base-Water-Molecule. Let’s consider Remove-base-Water-Molecule:Metonymy Example 2 (Continued)

- Given KM expression that can be translated into X-r-Y, where X, Y are frames, and r is a slot:

Do

X' = X, Y' = Y

I = Ideal(X, r)

M = MetonymySpace(Y)

Y = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

I = Ideal(Y, rinverse)

M = MetonymySpace(X)

X = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

Until (X = X' and Y = Y')

Return X'-r-Y'

- 1st iteration:
- X’ = Remove, Y’ = Water-Molecule

- I=(a Tangible-Entity;;type constraint
- with
- (purpose ((a Role with (in-event ((a Remove)))) ;;teleological constraint
- (has-part ((a Electron)))) ;; del-list
- M = {Water-Molecule, Oxygen-Atom, Hydrogen-Atom, Electron, …}
- Y = Oxygen-Atom
- I = (an Event}
- M = {Remove}
- X = Remove

- 2nd iteration:
- …
- Return: (a Remove with (object ((a Electron))) (base ((a Oxygen-Atom with (is-part-of ((a Water-Molecule)))))))

Given (a Nucleus with (content ((a Object)))), translate it into Nucleus-content-Object.Metonymy Example 3

- Given KM expression that can be translated into X-r-Y, where X, Y are frames, and r is a slot:

Do

X' = X, Y' = Y

I = Ideal(X, r)

M = MetonymySpace(Y)

Y = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

I = Ideal(Y, rinverse)

M = MetonymySpace(X)

X = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

Until (X = X' and Y = Y')

Return X'-r-Y'

1st iteration.

X' = Nucleus, Y' = Object

I = (a Tangible-Entity)

M = {Object }

Y = Object

I = (a Container)

M = {Nucleus, Container, Nucleoplasm, ...}

X = Container

2nd iteration:

…

Return: (a Nucleus with (purpose ((a Container with (content ((a Object)))))))

Given (a Catalyze with (instrument ((a Mitochondrion)))), translate it into Catalyze-instrument-Mitochondrion.Metonymy Example 4

- Given KM expression that can be translated into X-r-Y, where X, Y are frames, and r is a slot:

Do

X' = X, Y' = Y

I = Ideal(X, r)

M = MetonymySpace(Y)

Y = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

I = Ideal(Y, rinverse)

M = MetonymySpace(X)

X = m such that m Î M and distance(m, I) < distance(m', I) for all m' Î M

Until (X = X' and Y = Y')

Return X'-r-Y'

- 1st iteration:
- X' = Catalyze, Y' = Mitochondrion

- I = (a Chemical-Object with (purpose ((a Catalyst))))
- M = {Mitochondrion, Container, Aggregate, Oxido-Reductase, ...}
- Y = Oxido-Reductase
- I = (a Event)
- M = {Catalyze}
- X = Catalyze

- 2nd iteration:
- ...
- Return:(a Catalyze with (instrument ((a Oxido-Reductase with (element-type-of ((a Aggregate with (content-of ((a Be-Contained with (in-event-of ((a Container with (purpose-of ((a Mitochondrion)).

Future Work on Metonymy

- Test data bounded by KB. More data needed for evaluation.
- Other applications of the metonymy resolution algorithm.

Other Applications of Metonymy Resolution

- Shields SMEs from the idiosyncrasy of the representation:
- roles,
- spatial representations,
- aggregates,
- properties.
- E.g. instead of (a Car with (color ((a Color-Value with (value (:pair *red Object)))))), do (a Car with (color (*red))) directly
- LS generation for concise display of the knowledge to users.

Outline

- Loose speak overview
- Two types of LS:
- Complex nominal interpretation
- Metonymy
- Future work on LS

Future Work on LS Project

- Discover more patterns of LS.
- Overly general speak: stating knowledge in an overly general way, often using a general concept in the place of a specific one.
- Example: "there may be 15 [RNA] polymerases speeding along the same stretch of DNA ..".
- More extensive evaluations.
- Explore the process of theory and validation.

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